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: "Model Selection for Generalized Linear Models via GLIB, with Application to Epidemiology 1"
  Abstract: 1 This is the first draft of a chapter for Bayesian Biostatistics, edited by Donald A. Berry and Darlene K. Strangl. Adrian E. Raftery is Professor of Statistics and Sociology, Department of Statistics, GN-22, University of Washington, Seattle, WA 98195, USA. Sylvia Richardson is Directeur de Recherche, INSERM Unite 170, 16 avenue Paul Vaillant Couturier, 94807 Villejuif CEDEX, France. Raftery's research was supported by ONR contract no. N-00014-91-J-1074, by the Ministere de la Recherche et de l'Espace, Paris, by the Universite de Paris VI, and by INRIA, Rocquencourt, France. Raftery thanks the latter two institutions, Paul Deheuvels and Gilles Celeux for hearty hospitality during his Paris sabbatical in which part of this chapter was written. The authors are grateful to Christine Montfort for excellent research assistance and to Mariette Gerber, Michel Chavance and David Madigan for helpful 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"
    - "Bayesian Graphical Models for Discrete Data"
    - "Covariate Selection in Hierarchical Models of Hospital Admission Counts: A Bayes Factor Approach 1"
    - "Accounting for Model Uncertainty in Survival Analysis Improves Predictive Performance"
    - "Change Point and Change Curve Modeling in Stochastic Processes and Spatial Statistics"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "Bayesian Graphical Models for Discrete Data"):
  with members:
    - "Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models"
    - ""Linear Dependencies Represented by Chain Graphs," "Graphical Modelling With MIM," Manual. "Identifying Independence in Bayesian"
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"
    - "Accounting for Model Uncertainty in Survival Analysis Improves Predictive Performance"
    - "Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear Models"
    - "Decomposable graphical Gaussian model determination"
    - "Statistical Ideas for Selecting Network Architectures"
    - "Bayesian Graphical Modeling for Intelligent Tutoring Systems"

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