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: "Selection of Relevant Features and Examples in Machine Learning"
  Abstract: In this survey, we review work in machine learning on methods for handling data sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selecting relevant features, and the problem of selecting relevant examples. We describe the advances that have been made on these topics in both empirical and theoretical work in machine learning, and we present a general framework that we use to compare different methods. We close with some challenges for future work in this area.

Connected through hyperedge HE-1 (induced by source paper "Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Feature Subset Selection as Search with Probabilistic Estimates"
    - "MLC A Machine Learning Library in C"
    - "Issues in the Integration of Data Mining and Data Visualization Visualizing the Simple Bayesian Classifier"

Connected through hyperedge HE-2 (induced by source paper "Flexible Metric Nearest Neighbor Classiflcation"):
  with members:
    - "Adaptive Noise Injection for Input Variables Relevance Determination"
    - "An adaptation of Relief for attribute estimation in regression"
    - "Parzen. On estimation of a probability density function and mode. Annual Mathematical Statistics, 33:1065-1076, 1962."
    - "Virtual Seens and the Frequently Used Dataset"
    - "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"
    - "DART/HYESS Users Guide  recursive covering approach to local learning"

Connected through hyperedge HE-3 (induced by source paper "A Method of Combining Multiple Probabilistic Classifiers through Soft Competition on Different Feature Sets"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Combining estimates in regression and classification"
    - "Experiments with a New Boosting Algorithm"

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: Theory