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: "Building Classifiers using Bayesian Networks"
  Abstract: Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state of the art classifiers such as C4.5. This fact raises the question of whether a classifier with less restrictive assumptions can perform even better. In this paper we examine and evaluate approaches for inducing classifiers from data, based on recent results in the theory of learning Bayesian networks. Bayesian networks are factored representations of probability distributions that generalize the naive Bayes classifier and explicitly represent statements about independence. Among these approaches we single out a method we call Tree Augmented Naive Bayes (TAN), which outperforms naive Bayes, yet at the same time maintains the computational simplicity (no search involved) and robustness which are characteristic of naive Bayes. We experimentally tested these approaches using benchmark problems from the U. C. Irvine repository, and compared them against C4.5, naive Bayes, and wrapper-based feature selection methods.

Connected through hyperedge HE-1 (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"
    - "Toward Optimal Feature Selection"
    - "Searching for dependencies in Bayesian classifiers j A n V n j If the attributes"

Connected through hyperedge HE-2 (induced by source paper "BOOSTING AND NAIVE BAYESIAN LEARNING"):
  with members:
    - "Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods"
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Supervised and Unsupervised Discretization of Continuous Features"
    - "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier"

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