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: "Feature Selection by Means of a Feature Weighting Approach"
  Abstract: Selecting a set of features which is optimal for a given classification task is one of the central problems in machine learning. We address the problem using the flexible and robust filter technique EUBAFES. EUBAFES is based on a feature weighting approach which computes binary feature weights and therefore a solution in the feature selection sense and also gives detailed information about feature relevance by continuous weights. Moreover the user gets not only one but several potentially optimal feature subsets which is important for filter-based feature selection algorithms since it gives the flexibility to use even complex classifiers by the application of a combined filter/wrapper approach. We applied EUBAFES on a number of artificial and real world data sets and used radial basis function networks to examine the impact of the feature subsets to classifier accuracy and complexity.

Connected through hyperedge HE-1 (induced by source paper "Improving RBF Networks by the Feature Selection Approach EUBAFES"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"

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