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: "Efficient Learning of Selective Bayesian Network Classifiers"
  Abstract: In this paper, we present a computation-ally efficient method for inducing selective Bayesian network classifiers. Our approach is to use information-theoretic metrics to efficiently select a subset of attributes from which to learn the classifier. We explore three conditional, information-theoretic met-rics that are extensions of metrics used extensively in decision tree learning, namely Quin-lan's gain and gain ratio metrics and Man-taras's distance metric. We experimentally show that the algorithms based on gain ratio and distance metric learn selective Bayesian networks that have predictive accuracies as good as or better than those learned by existing selective Bayesian network induction approaches (K2-AS), but at a significantly lower computational cost. We prove that the subset-selection phase of these information-based algorithms has polynomial complexity, as compared to the worst-case exponential time complexity of the corresponding phase in K2-AS.

Connected through hyperedge HE-1 (induced by source paper "Toward Optimal Feature Selection"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Learning Limited Dependence Bayesian Classifiers"
    - "On the Greediness of Feature Selection Algorithms"
    - "A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers"

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

Connected through hyperedge HE-3 (induced by source paper "Induction of Selective Bayesian Classifiers"):
  with members:
    - "Learning NonLinearly Separable Boolean Functions With Linear Threshold Unit Trees and Madaline-Style Networks"
    - "Searching for dependencies in Bayesian classifiers j A n V n j If the attributes"
    - "Learning Bayesian Prototype Trees by Simulated Annealing"
    - "Learning in neural networks with Bayesian prototypes"
    - "MDL Learning of Probabilistic Neural Networks for Discrete Problem Domains"
    - "Recognition and Exploitation of Contextual Clues via Incremental Meta-Learning (Extended Version)"

Connected through hyperedge HE-4 (induced by source paper "A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers"):
  with members:
    - "Toward Optimal Feature Selection"
    - "Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management"
    - "Mining and Model Simplicity: A Case Study in Diagnosis"

Connected through hyperedge HE-5 (induced by source paper "28 Learning Bayesian Networks Using Feature Selection"):
  with members:
    - "Revising Bayesian Network Parameters Using Backpropagation"
    - "Mining and Model Simplicity: A Case Study in Diagnosis"

Connected through hyperedge HE-6 (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"
    - "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier"
    - "28 Learning Bayesian Networks Using Feature Selection"

Connected through hyperedge HE-7 (induced by source paper "An Algorithm for the Construction of Bayesian Network Structures from Data"):
  with members:
    - "Learning Bayesian Networks from Incomplete Data"
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"
    - "A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task"
    - "Learning Belief Networks from Data: An Information Theory Based Approach"
    - "An Algorithm for Bayesian Belief Network Construction from Data"
    - "Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management"

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