human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Separability is a Learner's Best Friend"):
  Abstract: Geometric separability is a generalisation of linear separability, familiar to many from Minsky and Papert's analysis of the Perceptron learning method. The concept forms a novel dimension along which to conceptualise learning methods. The present paper shows how geometric separability can be defined and demonstrates that it accurately predicts the performance of a at least one empirical learning method.

Includes members:
  Member "Parity: The Problem that Won't Go Away":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules")
  Member "There is No Free Lunch but the Starter is Cheap: Generalisation from First Principles":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Trading Spaces: Computation, Representation and the Limits of Uninformed Learning")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) belongs to. The 7 classes are: Neural_Networks, Reinforcement_Learning, Probabilistic_Methods, Theory, Genetic_Algorithms, Case_Based, Rule_Learning. Directly output the class name.

gpt: Theory