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: "An Algorithm for the Construction of Bayesian Network Structures from Data"
  Abstract: Previous algorithms for the construction of Bayesian belief network structures from data have been either highly dependent on conditional independence (CI) tests, or have required an ordering on the nodes to be supplied by the user. We present an algorithm that integrates these two approaches - CI tests are used to generate an ordering on the nodes from the database which is then used to recover the underlying Bayesian network structure using a non CI based method. Results of preliminary evaluation of the algorithm on two networks (ALARM and LED) are presented. We also discuss some algo rithm performance issues and open problems.

Connected through hyperedge HE-1 (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"
    - "Efficient Learning of Selective Bayesian Network Classifiers"

Connected through hyperedge HE-2 (induced by source paper "Learning Belief Networks from Data: An Information Theory Based Approach"):
  with members:
    - "A guide to the literature on learning probabilistic networks from data"
    - "An Algorithm for Bayesian Belief Network Construction from Data"

Connected through hyperedge HE-3 (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"
    - "Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management"
    - "Mining and Model Simplicity: A Case Study in Diagnosis"
    - "28 Learning Bayesian Networks Using Feature Selection"

Connected through hyperedge HE-4 (induced by source paper "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"):
  with members:
    - "Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models"
    - "The out-of-bootstrap method for model averaging and selection"
    - "Stacked Density Estimation"
    - "Bayesian Graphical Models for Discrete Data"
    - "Accounting for Model Uncertainty in Survival Analysis Improves Predictive Performance"
    - "Model Selection for Generalized Linear Models via GLIB, with Application to Epidemiology 1"
    - "Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear Models"
    - "Parallel Markov chain Monte Carlo sampling."

Connected through hyperedge HE-5 (induced by source paper "Learning Bayesian Networks from Incomplete Data"):
  with members:
    - "Supervised learning from incomplete data via an EM approach"
    - "A Tutorial on Learning With Bayesian Networks"

Connected through hyperedge HE-6 (induced by source paper "A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task"):
  with members:
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"
    - "A Probabilistic Calculus of Actions"
    - "On the Testability of Causal Models with Latent and Instrumental Variables"
    - "ASPECTS OF GRAPHICAL MODELS CONNECTED WITH CAUSALITY"
    - "Probabilistic evaluation of counterfactual queries"
    - "FROM BAYESIAN NETWORKS TO CAUSAL NETWORKS"
    - "MDL Learning of Probabilistic Neural Networks for Discrete Problem Domains"
    - "Two Algorithms for Inducing Structural Equation Models from Data"

Connected through hyperedge HE-7 (induced by source paper "An Algorithm for Bayesian Belief Network Construction from Data"):
  with members:
    - "Learning Belief Networks from Data: An Information Theory Based Approach"
    - "A guide to the literature on learning probabilistic networks from data"

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