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: "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.

Connected through hyperedge HE-1 (induced by source paper "NP-Completeness of Searches for Smallest Possible Feature Sets a subset of the set of all"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "On the Greediness of Feature Selection Algorithms"):
  with members:
    - "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"
    - "Toward Optimal Feature Selection"
    - "Efficient Locally Weighted Polynomial Regression Predictions"

Connected through hyperedge HE-3 (induced by source paper "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"):
  with members:
    - "Cost-sensitive feature reduction applied to a hybrid genetic algorithm"
    - "Growing Simpler Decision Trees to Facilitate Knowledge Discovery"
    - "Automated model selection"
    - "Oblivious Decision Trees and Abstract Cases"
    - "Generalizing from Case Studies: A Case Study"
    - "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"
    - "Compression-Based Feature Subset Selection  Keywords: Minimum Description Length Principle, Cross Validation, Noise"
    - "On the Greediness of Feature Selection Algorithms"

Connected through hyperedge HE-4 (induced by source paper "Theory Revision in Fault Hierarchies"):
  with members:
    - "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm"
    - "An Optimized Theory Revision Module"
    - "The Challenge of Revising an Impure Theory"
    - "An Improved Algorithm for Incremental Induction of Decision Trees"

Connected through hyperedge HE-5 (induced by source paper "Expectation-Based Selective Attention for Visual Monitoring and Control of a Robot Vehicle"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"

Connected through hyperedge HE-6 (induced by source paper "Toward Optimal Feature Selection"):
  with members:
    - "Learning Limited Dependence Bayesian Classifiers"
    - "On the Greediness of Feature Selection Algorithms"
    - "A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers"
    - "Efficient Learning of Selective Bayesian Network Classifiers"

Connected through hyperedge HE-7 (induced by source paper "Discovering Compressive Partial Determinations in Mixed Numerical and Symbolic Domains"):
  with members:
    - "On Biases in Estimating Multi-Valued Attributes"

Connected through hyperedge HE-8 (induced by source paper "Growing Simpler Decision Trees to Facilitate Knowledge Discovery"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"
    - "Rapid Quality Estimation of Neural Network Input Representations"

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