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: "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"
  Abstract: With the goal of reducing computational costs without sacrificing accuracy, we describe two algorithms to find sets of prototypes for nearest neighbor classification. Here, the term prototypes refers to the reference instances used in a nearest neighbor computation the instances with respect to which similarity is assessed in order to assign a class to a new data item. Both algorithms rely on stochastic techniques to search the space of sets of prototypes and are simple to implement. The first is a Monte Carlo sampling algorithm; the second applies random mutation hill climbing. On four datasets we show that only three or four prototypes sufficed to give predictive accuracy equal or superior to a basic nearest neighbor algorithm whose run-time storage costs were approximately 10 to 200 times greater. We briefly investigate how random mutation hill climbing may be applied to select features and prototypes simultaneously. Finally, we explain the performance of the sampling algorithm on these datasets in terms of a statistical measure of the extent of clustering displayed by the target classes.

Connected through hyperedge HE-1 (induced by source paper "On the Greediness of Feature Selection Algorithms"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Toward Optimal Feature Selection"
    - "Efficient Locally Weighted Polynomial Regression Predictions"

Connected through hyperedge HE-2 (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."
    - "Irrelevant Features and the Subset Selection Problem"
    - "Rapid Quality Estimation of Neural Network Input Representations"

Connected through hyperedge HE-3 (induced by source paper "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"):
  with members:
    - "Error Reduction through Learning Multiple Descriptions"
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Generating Accurate and Diverse Members of a Neural-Network Ensemble"
    - "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks"

Connected through hyperedge HE-4 (induced by source paper "Compression-Based Feature Subset Selection  Keywords: Minimum Description Length Principle, Cross Validation, Noise"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "The Power of Decision Tables"

Connected through hyperedge HE-5 (induced by source paper "PLEASE: A prototype learning system using genetic algorithms"):
  with members:
    - "A genetic prototype learner"

Connected through hyperedge HE-6 (induced by source paper "Automated model selection"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"

Connected through hyperedge HE-7 (induced by source paper "Cost-sensitive feature reduction applied to a hybrid genetic algorithm"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm"

Connected through hyperedge HE-8 (induced by source paper "Oblivious Decision Trees and Abstract Cases"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Using Decision Trees to Improve Case-Based Learning"
    - "Induction of Condensed Determinations"
    - "Average-Case Analysis of a Nearest Neighbor Algorithm"
    - "Fast NP Chunking Using Memory-Based Learning Techniques"

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: Case_Based