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: "Change Point and Change Curve Modeling in Stochastic Processes and Spatial Statistics"
  Abstract: 1 This article will appear in Volume 1, no. 4 (1994) of Journal of Applied Statistical Science. Adrian E. Raftery is Professor of Statistics and Sociology, Department of Statistics, GN-22, University of Washington, Seattle, WA 98195. This research was supported by ONR contract no. N-00014-91-J-1074, by NIH Grant no. 5R01HD26330-02, 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 this article was written. This article was prepared for presentation at the Conference on Applied Change Point Analysis, University of Maryland-Baltimore, March 17-18, 1993. Parts of this article review collaborative research with others to whom I would like to express my appreciation, namely Volkan Akman, Jeff Banfield, Nhu Le, Steven Lewis, Doug Martin, Fionn Murtagh, Ross Taplin and Simon Tavare.

Connected through hyperedge HE-1 (induced by source paper "Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models"):
  with members:
    - "BAYESIAN STATISTICS 6, pp. 000--000  Exact sampling for Bayesian inference: towards general purpose algorithms"
    - "On Sequential Simulation-Based Methods for Bayesian Filtering"
    - "Analysis of hospital quality monitors using hierarchical time series models"
    - "Priors and Component Structures in Autoregressive Time Series Models"
    - "FILTERING VIA SIMULATION: AUXILIARY PARTICLE FILTERS"
    - "Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions"
    - "BAYESIAN TIME SERIES: Models and Computations for the Analysis of Time Series in the Physical Sciences"
    - "STUDIES OF QUALITY MONITOR TIME SERIES: THE V.A. HOSPITAL SYSTEM build on foundational contributions in"

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"
    - "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"
    - "Model Selection for Generalized Linear Models via GLIB, with Application to Epidemiology 1"

Connected through hyperedge HE-3 (induced by source paper "A Note on the Dirichlet Process Prior in Bayesian Nonparametric Inference with Partial Exchangeability 1"):
  with members:
    - "A note on convergence rates of Gibbs sampling for nonparametric mixtures"

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