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: "Bayesian MARS"
  Abstract: A Bayesian approach to multivariate adaptive regression spline (MARS) fitting (Friedman, 1991) is proposed. This takes the form of a probability distribution over the space of possible MARS models which is explored using reversible jump Markov chain Monte Carlo methods (Green, 1995). The generated sample of MARS models produced is shown to have good predictive power when averaged and allows easy interpretation of the relative importance of predictors to the overall fit.

Connected through hyperedge HE-1 (induced by source paper "Automatic Smoothing Spline Projection Pursuit  Automatic Smoothing Spline Projection Pursuit."):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"

Connected through hyperedge HE-2 (induced by source paper "On Bayesian analysis of mixtures with an unknown number of components  Summary"):
  with members:
    - "BAYESIAN STATISTICS 6, pp. 000--000  Exact sampling for Bayesian inference: towards general purpose algorithms"
    - "FLEXIBLE PARAMETRIC MEASUREMENT ERROR MODELS"
    - "Reparameterisation Issues in Mixture Modelling and their bearing on MCMC algorithms"
    - "Decomposable graphical Gaussian model determination"
    - "Finding Overlapping Distributions with MML"

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