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 and Accounting for Model Uncertainty in Linear Regression Models"
  Abstract: 1 Adrian E. Raftery is Professor of Statistics and Sociology, David Madigan is Assistant Professor of Statistics, and Jennifer Hoeting is a Ph.D. Candidate, all at the Department of Statistics, GN-22, University of Washington, Seattle, WA 98195. The research of Raftery and Hoeting was supported by ONR Contract N-00014-91-J-1074. Madigan's research was partially supported by NSF grant no. DMS 92111627. The authors are grateful to Danika Lew for research assistance.

Connected through hyperedge HE-1 (induced by source paper "Bayesian inference for nondecomposable graphical Gaussian models"):
  with members:
    - ""Linear Dependencies Represented by Chain Graphs," "Graphical Modelling With MIM," Manual. "Identifying Independence in Bayesian"

Connected through hyperedge HE-2 (induced by source paper "Model Selection for Consumer Loan Application Data"):
  with members:
    - "Bayesian Model Selection in Social Research (with Discussion by"

Connected through hyperedge HE-3 (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"
    - "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"
    - "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-4 (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"
    - "Bayesian Graphical Models for Discrete Data"
    - "Decomposable graphical Gaussian model determination"

Connected through hyperedge HE-5 (induced by source paper "The out-of-bootstrap method for model averaging and selection"):
  with members:
    - "Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods"
    - "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"
    - "Bias, variance and prediction error for classification rules"

Connected through hyperedge HE-6 (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"
    - "Bayesian Graphical Models for Discrete Data"

Connected through hyperedge HE-7 (induced by source paper "Decomposable graphical Gaussian model determination"):
  with members:
    - "Bayesian Graphical Models for Discrete Data"
    - "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-8 (induced by source paper "Statistical Ideas for Selecting Network Architectures"):
  with members:
    - "Bayesian Graphical Models for Discrete Data"
    - "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"

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