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: "Toward Optimal Feature Selection"
  Abstract: In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for defining the theoretically optimal, but computation-ally intractable, method for feature subset selection is presented. We show that our goal should be to eliminate a feature if it gives us little or no additional information beyond that subsumed by the remaining features. In particular, this will be the case for both irrelevant and redundant features. We then give an efficient algorithm for feature selection which computes an approximation to the optimal feature selection criterion. The conditions under which the approximate algorithm is successful are examined. Empirical results are given on a number of data sets, showing that the algorithm effectively han dles datasets with large numbers of features.

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"
    - "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"
    - "Efficient Locally Weighted Polynomial Regression Predictions"

Connected through hyperedge HE-2 (induced by source paper "Efficient Learning of Selective Bayesian Network Classifiers"):
  with members:
    - "Induction of Selective Bayesian Classifiers"
    - "A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers"
    - "An Algorithm for the Construction of Bayesian Network Structures from Data"
    - "Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management"
    - "Mining and Model Simplicity: A Case Study in Diagnosis"
    - "28 Learning Bayesian Networks Using Feature Selection"

Connected through hyperedge HE-3 (induced by source paper "A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers"):
  with members:
    - "Efficient Learning of Selective Bayesian Network Classifiers"
    - "Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management"
    - "Mining and Model Simplicity: A Case Study in Diagnosis"

Connected through hyperedge HE-4 (induced by source paper "Learning Limited Dependence Bayesian Classifiers"):
  with members:
    - "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide"
    - "Building Classifiers using Bayesian Networks"
    - "Searching for dependencies in Bayesian classifiers j A n V n j If the attributes"

Connected through hyperedge HE-5 (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: Probabilistic_Methods