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: "Pattern Theoretic Feature Extraction and Constructive Induction"
  Abstract: This paper offers a perspective on features and pattern finding in general. This perspective is based on a robust complexity measure called Decomposed Function Car-dinality. A function decomposition algorithm for minimizing this complexity measure and finding the associated features is outlined. Results from experiments with this algorithm are also summarized.

Connected through hyperedge HE-1 (induced by source paper "APPLICATION OF ESOP MINIMIZATION IN MACHINE LEARNING AND KNOWLEDGE DISCOVERY"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "A dataset decomposition approach to data mining and machine discovery"):
  with members:
    - "Constructing Intermediate Concepts by Decomposition of Real Functions"
    - "Machine Learning by Function Decomposition"

Connected through hyperedge HE-3 (induced by source paper "Machine Learning by Function Decomposition"):
  with members:
    - "A dataset decomposition approach to data mining and machine discovery"
    - "Constructing Intermediate Concepts by Decomposition of Real Functions"

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