  Understanding galaxy properties may be the key to unlocking some of the most
intriguing mysteries of modern cosmology. Recent work relied on machine
learning to extract cosmological constraints on $\Omega_\mathrm{m}$ using only
one galaxy. But if this is true, how should we select the galaxy to use for
cosmology inference? In this paper, we consider selecting a galaxy that lies in
cosmic voids, the underdense regions of the cosmic web, and compare the
constraints obtained with the ones obtained when randomly selecting a galaxy in
the whole sample. We use the IllustrisTNG galaxy catalog from the CAMELS
project and the VIDE void finder to identify galaxies inside voids. We show
that void galaxies provide stronger constraints on $\Omega_\mathrm{m}$ compared
to randomly selected galaxies. This result suggests that the distinctive
characteristics of void galaxies may provide a cleaner and more effective
environment for extracting cosmological information.
