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: "Issues in the Integration of Data Mining and Data Visualization Visualizing the Simple Bayesian Classifier"
  Abstract: The simple Bayesian classifier (SBC), sometimes called Naive-Bayes, is built based on a conditional independence model of each attribute given the class. The model was previously shown to be surprisingly robust to obvious violations of this independence assumption, yielding accurate classification models even when there are clear conditional dependencies. The SBC can serve as an excellent tool for initial exploratory data analysis when coupled with a visualizer that makes its structure comprehensible. We describe such a visual representation of the SBC model that has been successfully implemented. We describe the requirements we had for such a visualization and the design decisions we made to satisfy them.

Connected through hyperedge HE-1 (induced by source paper "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier"):
  with members:
    - "BOOSTING AND NAIVE BAYESIAN LEARNING"
    - "On learning hierarchical classifications"
    - "Lazy Bayesian Trees"
    - "Mining and Model Simplicity: A Case Study in Diagnosis"

Connected through hyperedge HE-2 (induced by source paper "Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Feature Subset Selection as Search with Probabilistic Estimates"
    - "MLC A Machine Learning Library in C"
    - "Selection of Relevant Features and Examples in Machine Learning"

Connected through hyperedge HE-3 (induced by source paper "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"):
  with members:
    - "Towards a Better Understanding of Memory-Based Reasoning Systems"
    - "Induction of One-Level Decision Trees"
    - "Computational Learning in Humans and Machines"
    - "Average-Case Analysis of a Nearest Neighbor Algorithm"
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"
    - "Mining and Model Simplicity: A Case Study in Diagnosis"

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