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: "28 Learning Bayesian Networks Using Feature Selection"
  Abstract: This paper introduces a novel enhancement for learning Bayesian networks with a bias for small, high-predictive-accuracy networks. The new approach selects a subset of features that maximizes predictive accuracy prior to the network learning phase. We examine explicitly the effects of two aspects of the algorithm, feature selection and node ordering. Our approach generates networks that are computationally simpler to evaluate and display predictive accuracy comparable Bayesian networks are being increasingly recognized as an important representation for probabilistic reasoning. For many domains, the need to specify the probability distributions for a Bayesian network is considerable, and learning these probabilities from data using an algorithm like K2 [Cooper92] could alleviate such specification difficulties. We describe an extension to the Bayesian network learning approaches introduced in K2. Our goal is to construct networks that are simpler to evaluate but still have high predictive accuracy relative to networks that model all features. Rather than use all database features (or attributes) for constructing the network, we select a subset of features that maximize the predictive accuracy of the network. Then the learning process uses only the selected features as nodes in learning the Bayesian network. We examine explicitly the effects of two aspects of the algorithm: (a) feature selection, and (b) node ordering. Our experimental results verify that this approach generates networks that are compu-tationally simpler to evaluate and display predictive accuracy comparable to the predictive accuracy of Bayesian networks that model all features. Our results, similar to those observed by other studies of feature selection in learning [Caruana94, John94, Langley94a, Langley94b], demonstrate that feature selection provides comparable predictive accuracy using smaller networks. For example, by selecting as few as 18% of the features for the to that of Bayesian networks which model all attributes.

Connected through hyperedge HE-1 (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"
    - "Mining and Model Simplicity: A Case Study in Diagnosis"

Connected through hyperedge HE-2 (induced by source paper "Revising Bayesian Network Parameters Using Backpropagation"):
  with members:
    - "Theory Refinement Combining Analytical and Empirical Methods"
    - "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases"
    - "Automated Refinement of First-Order Horn-Clause Domain Theories"

Connected through hyperedge HE-3 (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"
    - "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