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: "Covariate Selection in Hierarchical Models of Hospital Admission Counts: A Bayes Factor Approach 1"
  Abstract: TECHNICAL REPORT No. 268 Department of Statistics, GN-22 University of Washington Seattle, Washington 98195 USA 1 Susan L. Rosenkranz is Pew Health Policy Postdoctoral Fellow at the Institute for Health Policy Studies, Box 0936, University of California at San Francisco, San Francisco, CA 94143, and Adrian E. Raftery is Professor of Statistics and Sociology, Department of Statistics, GN-22, University of Washington, Seattle, WA 98195. Rosenkranz's research was supported by the National Research Service Award 5T32CA 09168-17 from the National Cancer Institute. The authors are grateful to Paula Diehr and Kevin Cain 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"
    - "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"

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