  $Context.$ Core-collapse supernovae (CCSNe) are expected to emit
gravitational wave signals that could be detected by current and future
generation interferometers within the Milky Way and nearby galaxies. The
stochastic nature of the signal arising from CCSNe requires alternative
detection methods to matched filtering. $Aims.$ We aim to show the potential of
machine learning (ML) for multi-label classification of different CCSNe
simulated signals and noise transients using real data. We compared the
performance of 1D and 2D convolutional neural networks (CNNs) on single and
multiple detector data. For the first time, we tested multi-label
classification also with long short-term memory (LSTM) networks. $Methods.$ We
applied a search and classification procedure for CCSNe signals, using an event
trigger generator, the Wavelet Detection Filter (WDF), coupled with ML. We used
time series and time-frequency representations of the data as inputs to the ML
models. To compute classification accuracies, we simultaneously injected, at
detectable distance of 1\,kpc, CCSN waveforms, obtained from recent
hydrodynamical simulations of neutrino-driven core-collapse, onto
interferometer noise from the O2 LIGO and Virgo science run. $Results.$ We
compared the performance of the three models on single detector data. We then
merged the output of the models for single detector classification of noise and
astrophysical transients, obtaining overall accuracies for LIGO ($\sim99\%$)
and ($\sim80\%$) for Virgo. We extended our analysis to the multi-detector case
using triggers coincident among the three ITFs and achieved an accuracy of
$\sim98\%$.
