We approach particle reconstruction in collider experiments as a set-to-set problem and show the efficacy of a deep-learning model that predicts hypergraph incidence structure. This model outperforms a benchmark parameterized algorithm in predicting the momentum of particle jets and shows an ability to disentangle individual neutral particles in the collimated environment. Representing particles as hyperedges on the set of input nodes introduces an inductive bias that predisposes the predictions to conserve energy and thus promotes accurate, interpretable results.