human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "Parity: The Problem that Won't Go Away"):
  with members:
    - "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"
    - "Truth-from-Trash Learning and the Mobot"
    - "Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules"

Connected through hyperedge HE-2 (induced by source paper "There is No Free Lunch but the Starter is Cheap: Generalisation from First Principles"):
  with members:
    - "Trading Spaces: Computation, Representation and the Limits of Uninformed Learning"
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."

Question: Please tell me which class the center node 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