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 Graphical Models for Discrete Data"
  Abstract: z York's research was supported by a NSF graduate fellowship. The authors are grateful to Julian Besag, David Bradshaw, Jeff Bradshaw, James Carlsen, David Draper, Ivar Heuch, Robert Kass, Augustine Kong, Steffen Lauritzen, Adrian Raftery, and James Zidek for helpful comments and discussions.

Connected through hyperedge HE-1 (induced by source paper "Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models"):
  with members:
    - "Estimating Bayes Factors via Posterior Simulation with the Laplace-Metropolis Estimator"
    - "Inference in Model-Based Cluster Analysis"
    - "Formal Rules for Selecting Prior Distributions: A Review and Annotated Bibliography"
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"
    - "Covariate Selection in Hierarchical Models of Hospital Admission Counts: A Bayes Factor Approach 1"
    - "Accounting for Model Uncertainty in Survival Analysis Improves Predictive Performance"
    - "Model Selection for Generalized Linear Models via GLIB, with Application to Epidemiology 1"
    - "Change Point and Change Curve Modeling in Stochastic Processes and Spatial Statistics"

Connected through hyperedge HE-2 (induced by source paper "Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear Models"):
  with members:
    - "Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models"
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"
    - "Decomposable graphical Gaussian model determination"

Connected through hyperedge HE-3 (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"
    - "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."
    - "Decomposable graphical Gaussian model determination"

Connected through hyperedge HE-4 (induced by source paper "Accounting for Model Uncertainty in Survival Analysis Improves Predictive Performance"):
  with members:
    - "Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models"
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"

Connected through hyperedge HE-5 (induced by source paper "Decomposable graphical Gaussian model determination"):
  with members:
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"
    - "Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear Models"
    - "On Bayesian analysis of mixtures with an unknown number of components  Summary"

Connected through hyperedge HE-6 (induced by source paper "Statistical Ideas for Selecting Network Architectures"):
  with members:
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"
    - "What Size Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation"
    - "Lessons in Neural Network Training: Overfitting Lessons in Neural Network Training: Overfitting May be Harder"

Connected through hyperedge HE-7 (induced by source paper "Bayesian Graphical Modeling for Intelligent Tutoring Systems"):
  with members:
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"
    - "Introduction to the Special Section on Knowledge-Based Construction of Probabilistic and Decision Models (IEEE Transactions"

Connected through hyperedge HE-8 (induced by source paper ""Linear Dependencies Represented by Chain Graphs," "Graphical Modelling With MIM," Manual. "Identifying Independence in Bayesian"):
  with members:
    - "An Alternative Markov Property for Chain Graphs"
    - "ASPECTS OF GRAPHICAL MODELS CONNECTED WITH CAUSALITY"
    - "FROM BAYESIAN NETWORKS TO CAUSAL NETWORKS"
    - "Bayesian inference for nondecomposable graphical Gaussian models"

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