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: "Monte Carlo Approach to Bayesian Regression Modeling"
  Abstract: In the framework of a functional response model (i.e. a regression model, or a feedforward neural network) an estimator of a nonlinear response function is constructed from a set of functional units. The parameters defining these functional units are estimated using the Bayesian approach. A sample representing the Bayesian posterior distribution is obtained by applying the Markov chain Monte Carlo procedure, namely the combination of Gibbs and Metropolis-Hastings algorithms. The method is described for histogram, B-spline and radial basis function estimators of a response function. In general, the proposed approach is suitable for finding Bayes-optimal values of parameters in a complicated parameter space. We illustrate the method on numerical examples.

Connected through hyperedge HE-1 (induced by source paper "ON MCMC METHODS IN BAYESIAN REGRESSION ANALYSIS AND MODEL SELECTION"):
  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