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: "PLEASE: A prototype learning system using genetic algorithms"
  Abstract: Prototypes have been proposed as representation of concepts that are used effectively by humans. Developing computational schemes for generating prototypes from examples, however, has proved to be a difficult problem. We present a novel genetic algorithm based prototype learning system, PLEASE, for constructing appropriate prototypes from classified training instances. After constructing a set of prototypes for each of the possible classes, the class of a new input instance is determined by the nearest prototype to this instance. Attributes are assumed to be ordinal in nature and prototypes are represented as sets of feature-value pairs. A genetic algorithm is used to evolve the number of prototypes per class and their positions on the input space. We present experimental results on a series of artificial problems of varying complexity. PLEASE performs competitively with several nearest neighbor classification algorithms on the problem set. An analysis of the strengths and weaknesses of the initial version of our system motivates the need for additional operators. The inclusion of these operators substantially improves the performance of the system on particularly difficult problems.

Connected through hyperedge HE-1 (induced by source paper "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "A genetic prototype learner"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Using Real-Valued Genetic Algorithms to Evolve Rule Sets for Classification"

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