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: "Cost-sensitive feature reduction applied to a hybrid genetic algorithm"
  Abstract: This study is concerned with whether it is possible to detect what information contained in the training data and background knowledge is relevant for solving the learning problem, and whether irrelevant information can be eliminated in preprocessing before starting the learning process. A case study of data preprocessing for a hybrid genetic algorithm shows that the elimination of irrelevant features can substantially improve the efficiency of learning. In addition, cost-sensitive feature elimination can be effective for reducing costs of induced hypotheses.

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"
    - "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-2 (induced by source paper "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm"):
  with members:
    - "Theory Revision in Fault Hierarchies"
    - "Boosting Trees for Cost-Sensitive Classifications"
    - "Learning Active Classifiers"
    - "A systematic description of greedy optimisation algorithms for cost sensitive generalisation"
    - "Predicting Ordinal Classes in ILP"
    - "How to Get a Free Lunch: A Simple Cost Model for Machine Learning Applications"
    - "Evolution, Learning, and Instinct: 100 Years of the Baldwin Effect Using Learning to Facilitate the"

Connected through hyperedge HE-3 (induced by source paper "Irrelevant Features and the Subset Selection Problem"):
  with members:
    - "Theory Revision in Fault Hierarchies"
    - "An Optimized Theory Revision Module"
    - "Expectation-Based Selective Attention for Visual Monitoring and Control of a Robot Vehicle"
    - "Feature Subset Selection as Search with Probabilistic Estimates"
    - "NP-Completeness of Searches for Smallest Possible Feature Sets a subset of the set of all"
    - "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"
    - "Machine Learning Methods for International Conflict Databases: A Case Study in Predicting Mediation Outcome"
    - "The Effective Size of a Neural Network: A Principal Component Approach"

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: Rule_Learning