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: "A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers"
  Abstract: In this paper we present a novel induction algorithm for Bayesian networks. This selective Bayesian network classifier selects a subset of attributes that maximizes predictive accuracy prior to the network learning phase, thereby learning Bayesian networks with a bias for small, high-predictive-accuracy networks. We compare the performance of this classifier with selective and non-selective naive Bayesian classifiers. We show that the selective Bayesian network classifier performs significantly better than both versions of the naive Bayesian classifier on almost all databases analyzed, and hence is an enhancement of the naive Bayesian classifier. Relative to the non-selective Bayesian network classifier, our selective Bayesian network classifier generates networks that are computationally simpler to evaluate and that display predictive accuracy comparable to that of Bayesian networks which model all features.

Connected through hyperedge HE-1 (induced by source paper "Toward Optimal Feature Selection"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Learning Limited Dependence Bayesian Classifiers"
    - "On the Greediness of Feature Selection Algorithms"
    - "Efficient Learning of Selective Bayesian Network Classifiers"

Connected through hyperedge HE-2 (induced by source paper "Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management"):
  with members:
    - "Efficient Learning of Selective Bayesian Network Classifiers"
    - "An Algorithm for the Construction of Bayesian Network Structures from Data"

Connected through hyperedge HE-3 (induced by source paper "Efficient Learning of Selective Bayesian Network Classifiers"):
  with members:
    - "Toward Optimal Feature Selection"
    - "Induction of 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-4 (induced by source paper "Mining and Model Simplicity: A Case Study in Diagnosis"):
  with members:
    - "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"
    - "Efficient Learning of Selective Bayesian Network Classifiers"
    - "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier"
    - "28 Learning Bayesian Networks Using Feature Selection"

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