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: "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier"
  Abstract: The simple Bayesian classifier (SBC) is commonly thought to assume that attributes are independent given the class, but this is apparently contradicted by the surprisingly good performance it exhibits in many domains that contain clear attribute dependences. No explanation for this has been proposed so far. In this paper we show that the SBC does not in fact assume attribute independence, and can be optimal even when this assumption is violated by a wide margin. The key to this finding lies in the distinction between classification and probability estimation: correct classification can be achieved even when the probability estimates used contain large errors. We show that the previously-assumed region of optimality of the SBC is a second-order infinitesimal fraction of the actual one. This is followed by the derivation of several necessary and several sufficient conditions for the optimality of the SBC. For example, the SBC is optimal for learning arbitrary conjunctions and disjunctions, even though they violate the independence assumption. The paper also reports empirical evidence of the SBC's competitive performance in domains containing substantial degrees of attribute dependence.

Connected through hyperedge HE-1 (induced by source paper "Issues in the Integration of Data Mining and Data Visualization Visualizing the Simple Bayesian Classifier"):
  with members:
    - "Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology"
    - "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"

Connected through hyperedge HE-2 (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"
    - "A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers"
    - "Efficient Learning of Selective Bayesian Network Classifiers"
    - "28 Learning Bayesian Networks Using Feature Selection"

Connected through hyperedge HE-3 (induced by source paper "On learning hierarchical classifications"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Bias Plus Variance Decomposition for Zero-One Loss Functions"
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"

Connected through hyperedge HE-4 (induced by source paper "Lazy Bayesian Trees"):
  with members:
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"
    - "Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid"

Connected through hyperedge HE-5 (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"
    - "Building Classifiers using Bayesian Networks"
    - "Supervised and Unsupervised Discretization of Continuous Features"

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: Case_Based