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: "A dataset decomposition approach to data mining and machine discovery"
  Abstract: We present a novel data mining approach based on decomposition. In order to analyze a given dataset, the method decomposes it to a hierarchy of smaller and less complex datasets that can be analyzed independently. The method is experimentally evaluated on a real-world housing loans allocation dataset, showing that the decomposition can (1) discover meaningful intermediate concepts, (2) decompose a relatively complex dataset to datasets that are easy to analyze and comprehend, and (3) derive a classifier of high classification accuracy. We also show that human interaction has a positive effect on both the comprehensibility and classification accuracy.

Connected through hyperedge HE-1 (induced by source paper "Constructing Intermediate Concepts by Decomposition of Real Functions"):
  with members:
    - "Machine Learning by Function Decomposition"

Connected through hyperedge HE-2 (induced by source paper "Pattern Theoretic Feature Extraction and Constructive Induction"):
  with members:
    - "Machine Learning by Function Decomposition"
    - "APPLICATION OF ESOP MINIMIZATION IN MACHINE LEARNING AND KNOWLEDGE DISCOVERY"

Connected through hyperedge HE-3 (induced by source paper "Machine Learning by Function Decomposition"):
  with members:
    - "Constructing Intermediate Concepts by Decomposition of Real Functions"
    - "Pattern Theoretic Feature Extraction and Constructive Induction"

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