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: "Mining and Model Simplicity: A Case Study in Diagnosis"
  Abstract: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD), 1996. The official version of this paper has been published by the American Association for Artificial Intelligence (http://www.aaai.org) c fl 1996, American Association for Artificial Intelligence. All rights reserved. Abstract We describe the results of performing data mining on a challenging medical diagnosis domain, acute abdominal pain. This domain is well known to be difficult, yielding little more than 60% predictive accuracy for most human and machine diagnosticians. Moreover, many researchers argue that one of the simplest approaches, the naive Bayesian classifier, is optimal. By comparing the performance of the naive Bayesian classifier to its more general cousin, the Bayesian network classifier, and to selective Bayesian classifiers with just 10% of the total attributes, we show that the simplest models perform at least as well as the more complex models. We argue that simple models like the selective naive Bayesian classifier will perform as well as more complicated models for similarly complex domains with relatively small data sets, thereby calling into question the extra expense necessary to induce more complex models.

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"
    - "Issues in the Integration of Data Mining and Data Visualization Visualizing the Simple Bayesian Classifier"

Connected through hyperedge HE-2 (induced by source paper "Efficient Learning of Selective Bayesian Network Classifiers"):
  with members:
    - "Toward Optimal Feature Selection"
    - "Induction of Selective Bayesian Classifiers"
    - "A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers"
    - "An Algorithm for the Construction of Bayesian Network Structures from Data"
    - "Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management"
    - "28 Learning Bayesian Networks Using Feature Selection"

Connected through hyperedge HE-3 (induced by source paper "A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers"):
  with members:
    - "Toward Optimal Feature Selection"
    - "Efficient Learning of Selective Bayesian Network Classifiers"
    - "Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management"

Connected through hyperedge HE-4 (induced by source paper "28 Learning Bayesian Networks Using Feature Selection"):
  with members:
    - "Revising Bayesian Network Parameters Using Backpropagation"
    - "Efficient Learning of Selective Bayesian Network Classifiers"

Connected through hyperedge HE-5 (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"
    - "Issues in the Integration of Data Mining and Data Visualization Visualizing 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