human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Toward Optimal Feature Selection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "On the Greediness of Feature Selection Algorithms")
  Member "Efficient Learning of Selective Bayesian Network Classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Toward Optimal Feature Selection")
  Member "Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Efficient Learning of Selective Bayesian Network Classifiers")
  Member "Mining and Model Simplicity: A Case Study in Diagnosis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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