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: "Inference in Model-Based Cluster Analysis"
  Abstract: Technical Report no. 285 Department of Statistics University of Washington. March 10, 1995

Connected through hyperedge HE-1 (induced by source paper "Detecting Features in Spatial Point Processes with Clutter via Model-Based Clustering"):
  with members:
    - "Principal Curve Clustering With Noise"

Connected through hyperedge HE-2 (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"
    - "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"
    - "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-3 (induced by source paper "Estimating Bayes Factors via Posterior Simulation with the Laplace-Metropolis Estimator"):
  with members:
    - "Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear 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