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: "Bayesian and Information-Theoretic Priors for Bayesian Network Parameters"
  Abstract: We consider Bayesian and information-theoretic approaches for determining non-informative prior distributions in a parametric model family. The information-theoretic approaches are based on the recently modified definition of stochastic complexity by Rissanen, and on the Minimum Message Length (MML) approach by Wallace. The Bayesian alternatives include the uniform prior, and the equivalent sample size priors. In order to be able to empirically compare the different approaches in practice, the methods are instantiated for a model family of practical importance, the family of Bayesian networks.

Connected through hyperedge HE-1 (induced by source paper "Stochastic Complexity Based Estimation of Missing Elements in Questionnaire Data"):
  with members:
    - "MDL and MML Similarities and Differences (Introduction to Minimum Encoding Inference Part III)"

Connected through hyperedge HE-2 (induced by source paper "MDL and MML Similarities and Differences (Introduction to Minimum Encoding Inference Part III)"):
  with members:
    - "Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models"
    - "A Practical Bayesian Framework for Backprop Networks"
    - "Finding Overlapping Distributions with MML"
    - "On Pruning and Averaging Decision Trees"
    - "Unsupervised Learning Using MML"
    - "BAYESIAN ESTIMATION OF THE VON MISES CONCENTRATION PARAMETER"
    - "SINGLE FACTOR ANALYSIS BY MML ESTIMATION"
    - "Stochastic Complexity Based Estimation of Missing Elements in Questionnaire Data"

Connected through hyperedge HE-3 (induced by source paper "A Tutorial on Learning With Bayesian Networks"):
  with members:
    - "LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE"
    - "Learning Bayesian Networks from Incomplete Data"
    - "Sequential Update of Bayesian Network Structure"
    - "Automated Decomposition of Model-based Learning Problems"
    - "Bayesian Finite Mixtures for Nonlinear Modeling of Educational data"
    - "On the Sample Complexity of Learning Bayesian Networks"
    - "Update rules for parameter estimation in Bayesian networks"
    - "Discovering Structure in Continuous Variables Using Bayesian Networks"

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