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: "ON MCMC METHODS IN BAYESIAN REGRESSION ANALYSIS AND MODEL SELECTION"
  Abstract: The objective of statistical data analysis is not only to describe the behaviour of a system, but also to propose, construct (and then to check) a model of observed processes. Bayesian methodology offers one of possible approaches to estimation of unknown components of the model (its parameters or functional components) in a framework of a chosen model type. However, in many instances the evaluation of Bayes posterior distribution (which is basal for Bayesian solutions) is difficult and practically intractable (even with the help of numerical approximations). In such cases the Bayesian analysis may be performed with the help of intensive simulation techniques called the `Markov chain Monte Carlo'. The present paper reviews the best known approaches to MCMC generation. It deals with several typical situations of data analysis and model construction where MCMC methods have been successfully applied. Special attention is devoted to the problem of selection of optimal regression model constructed from regression splines or from other functional units.

Connected through hyperedge HE-1 (induced by source paper "Monte Carlo Approach to Bayesian Regression Modeling"):
  with members:
    (none)

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