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 "Irrelevant Features and the Subset Selection Problem"):
  Abstract: We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small high-accuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features into useful categories of relevance. We present definitions for irrelevance and for two degrees of relevance. These definitions improve our understanding of the behavior of previous subset selection algorithms, and help define the subset of features that should be sought. The features selected should depend not only on the features and the target concept, but also on the induction algorithm. We describe a method for feature subset selection using cross-validation that is applicable to any induction algorithm, and discuss experiments conducted with ID3 and C4.5 on artificial and real datasets.

Includes members:
  Member "Theory Revision in Fault Hierarchies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "An Optimized Theory Revision Module")
  Member "An Optimized Theory Revision Module":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Theory Revision in Fault Hierarchies")
  Member "Expectation-Based Selective Attention for Visual Monitoring and Control of a Robot Vehicle":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Hierarchical Mixtures of Experts and the EM Algorithm")
  Member "Feature Subset Selection as Search with Probabilistic Estimates":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation")
  Member "NP-Completeness of Searches for Smallest Possible Feature Sets a subset of the set of all":
    also connected through other hyperedges:
      (none)
  Member "Cost-sensitive feature reduction applied to a hybrid genetic algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms")
  Member "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "On the Greediness of Feature Selection Algorithms")
  Member "Machine Learning Methods for International Conflict Databases: A Case Study in Predicting Mediation Outcome":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "Knowledge Discovery in International Conflict Databases")

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