  We introduce 'Class Symbolic Regression' (Class SR) a first framework for
automatically finding a single analytical functional form that accurately fits
multiple datasets - each realization being governed by its own (possibly)
unique set of fitting parameters. This hierarchical framework leverages the
common constraint that all the members of a single class of physical phenomena
follow a common governing law. Our approach extends the capabilities of our
earlier Physical Symbolic Optimization ($\Phi$-SO) framework for Symbolic
Regression, which integrates dimensional analysis constraints and deep
reinforcement learning for unsupervised symbolic analytical function discovery
from data. Additionally, we introduce the first Class SR benchmark, comprising
a series of synthetic physical challenges specifically designed to evaluate
such algorithms. We demonstrate the efficacy of our novel approach by applying
it to these benchmark challenges and showcase its practical utility for
astrophysics by successfully extracting an analytic galaxy potential from a set
of simulated orbits approximating stellar streams.
