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: "On the Greediness of Feature Selection Algorithms"
  Abstract: Based on our analysis and experiments using real-world datasets, we find that the greediness of forward feature selection algorithms does not severely corrupt the accuracy of function approximation using the selected input features, but improves the efficiency significantly. Hence, we propose three greedier algorithms in order to further enhance the efficiency of the feature selection processing. We provide empirical results for linear regression, locally weighted regression and k-nearest-neighbor models. We also propose to use these algorithms to develop an offline Chinese and Japanese handwriting recognition system with auto matically configured, local models.

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 "Toward Optimal Feature Selection"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Learning Limited Dependence Bayesian Classifiers"
    - "A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers"
    - "Efficient Learning of Selective Bayesian Network Classifiers"

Connected through hyperedge HE-3 (induced by source paper "Efficient Locally Weighted Polynomial Regression Predictions"):
  with members:
    - "Value Function Based Production Scheduling"
    - "Category: Control, Navigation and Planning Preference: Oral presentation Exploiting Model Uncertainty Estimates for Safe Dynamic"
    - "Control Systems Magazine, 14, 1, pp.57-71. Robot Juggling: An Implementation of Memory-based Learning"
    - "Bumptrees for Efficient Function, Constraint, and Classification Learning"

Connected through hyperedge HE-4 (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"
    - "Cost-sensitive feature reduction applied to a hybrid genetic algorithm"
    - "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"

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