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: "Learning Limited Dependence Bayesian Classifiers"
  Abstract: We present a framework for characterizing Bayesian classification methods. This framework can be thought of as a spectrum of allowable dependence in a given probabilistic model with the Naive Bayes algorithm at the most restrictive end and the learning of full Bayesian networks at the most general extreme. While much work has been carried out along the two ends of this spectrum, there has been surprising little done along the middle. We analyze the assumptions made as one moves along this spectrum and show the tradeoffs between model accuracy and learning speed which become critical to consider in a variety of data mining domains. We then present a general induction algorithm that allows for traversal of this spectrum depending on the available computational power for carrying out induction and show its application in a number of domains with different properties.

Connected through hyperedge HE-1 (induced by source paper "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide"):
  with members:
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Robustness Analysis of Bayesian Networks with Finitely Generated Convex Sets of Distributions"
    - "Chain graphs for learning"
    - "Automated Decomposition of Model-based Learning Problems"
    - "Update rules for parameter estimation in Bayesian networks"
    - "Discovering Structure in Continuous Variables Using Bayesian Networks"
    - "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View"

Connected through hyperedge HE-2 (induced by source paper "Searching for dependencies in Bayesian classifiers j A n V n j If the attributes"):
  with members:
    - "Induction of Selective Bayesian Classifiers"

Connected through hyperedge HE-3 (induced by source paper "Toward Optimal Feature Selection"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "On the Greediness of Feature Selection Algorithms"
    - "A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers"
    - "Efficient Learning of Selective Bayesian Network Classifiers"

Connected through hyperedge HE-4 (induced by source paper "Building Classifiers using Bayesian Networks"):
  with members:
    - "BOOSTING AND NAIVE BAYESIAN LEARNING"

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