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: "Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models"
  Abstract: Technical Report no. 255 Department of Statistics, University of Washington August 1993; Revised March 1994

Connected through hyperedge HE-1 (induced by source paper "Formal Rules for Selecting Prior Distributions: A Review and Annotated Bibliography"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Inference in Model-Based Cluster Analysis"):
  with members:
    - "Estimating Bayes Factors via Posterior Simulation with the Laplace-Metropolis Estimator"
    - "Detecting Features in Spatial Point Processes with Clutter via Model-Based Clustering"

Connected through hyperedge HE-3 (induced by source paper "Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear Models"):
  with members:
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"
    - "Bayesian Graphical Models for Discrete Data"
    - "Decomposable graphical Gaussian model determination"

Connected through hyperedge HE-4 (induced by source paper "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"):
  with members:
    - "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"
    - "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-5 (induced by source paper "Accounting for Model Uncertainty in Survival Analysis Improves Predictive Performance"):
  with members:
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"
    - "Bayesian Graphical Models for Discrete Data"

Connected through hyperedge HE-6 (induced by source paper "Change Point and Change Curve Modeling in Stochastic Processes and Spatial Statistics"):
  with members:
    - "Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models"
    - "A Note on the Dirichlet Process Prior in Bayesian Nonparametric Inference with Partial Exchangeability 1"

Connected through hyperedge HE-7 (induced by source paper "MDL and MML Similarities and Differences (Introduction to Minimum Encoding Inference Part III)"):
  with members:
    - "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"
    - "Bayesian and Information-Theoretic Priors for Bayesian Network Parameters"
    - "Stochastic Complexity Based Estimation of Missing Elements in Questionnaire Data"

Connected through hyperedge HE-8 (induced by source paper "Bayesian Graphical Models for Discrete Data"):
  with members:
    - ""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"
    - "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"
    - "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