  The deep learning field is growing rapidly as witnessed by the exponential
growth of papers submitted to journals, conferences, and pre-print servers. To
cope with the sheer number of papers, several text mining tools from natural
language processing (NLP) have been proposed that enable researchers to keep
track of recent findings. In this context, our paper makes two main
contributions: first, we collected and annotated a dataset of papers paired by
title and sub-field from the field of artificial intelligence (AI), and,
second, we present results on how to predict a paper's AI sub-field from a
given paper title only. Importantly, for the latter, short-text classification
task we compare several algorithms from conventional machine learning all the
way up to recent, larger transformer architectures. Finally, for the
transformer models, we also present gradient-based, attention visualizations to
further explain the model's classification process. All code can be found at
\url{https://github.com/1pha/paperswithtopic}
