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: "Hybrid Learning Using Genetic Algorithms and Decision Trees for Pattern Classification"
  Abstract: This paper introduces a hybrid learning methodology that integrates genetic algorithms (GAs) and decision tree learning (ID3) in order to evolve optimal subsets of discriminatory features for robust pattern classification. A GA is used to search the space of all possible subsets of a large set of candidate discrimination features. For a given feature subset, ID3 is invoked to produce a decision tree. The classification performance of the decision tree on unseen data is used as a measure of fitness for the given feature set, which, in turn, is used by the GA to evolve better feature sets. This GA-ID3 process iterates until a feature subset is found with satisfactory classification performance. Experimental results are presented which illustrate the feasibility of our approach on difficult problems involving recognizing visual concepts in satellite and facial image data. The results also show improved classification performance and reduced description complexity when compared against standard methods for feature selection.

Connected through hyperedge HE-1 (induced by source paper "Evolution, Learning, and Instinct: 100 Years of the Baldwin Effect Using Learning to Facilitate the"):
  with members:
    - "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm"
    - "Data Analyses Using Simulated Breeding and Inductive Learning Methods"
    - "Evolving Visual Routines"

Connected through hyperedge HE-2 (induced by source paper "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "A theory of questions and question asking"
    - "In Machine Learning: A Multistrategy Approach, Vol. IV  Macro and Micro Perspectives of Multistrategy Learning"
    - "The Use of Explicit Goals for Knowledge to Guide Inference and Learning"
    - "Issues in Goal-Driven Explanation"
    - "Context-Based Similarity Applied to Retrieval of Relevant Cases"
    - "Machine Learning and Inference"
    - "Machine Learning and Inference"

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: Genetic_Algorithms