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: "Comparing Bayesian Model Class Selection Criteria by Discrete Finite Mixtures"
  Abstract: We investigate the problem of computing the posterior probability of a model class, given a data sample and a prior distribution for possible parameter settings. By a model class we mean a group of models which all share the same parametric form. In general this posterior may be very hard to compute for high-dimensional parameter spaces, which is usually the case with real-world applications. In the literature several methods for computing the posterior approximately have been proposed, but the quality of the approximations may depend heavily on the size of the available data sample. In this work we are interested in testing how well the approximative methods perform in real-world problem domains. In order to conduct such a study, we have chosen the model family of finite mixture distributions. With certain assumptions, we are able to derive the model class posterior analytically for this model family. We report a series of model class selection experiments on real-world data sets, where the true posterior and the approximations are compared. The empirical results support the hypothesis that the approximative techniques can provide good estimates of the true posterior, especially when the sample size grows large.

Connected through hyperedge HE-1 (induced by source paper "Comparing Predictive Inference Methods for Discrete Domains"):
  with members:
    - "Constructing Bayesian finite mixture models by the EM algorithm"
    - "Probabilistic Instance-Based Learning"

Connected through hyperedge HE-2 (induced by source paper "Bayesian Finite Mixtures for Nonlinear Modeling of Educational data"):
  with members:
    - "A Tutorial on Learning With Bayesian Networks"
    - "EXPERIMENTING WITH THE CHEESEMAN-STUTZ EVIDENCE APPROXIMATION FOR PREDICTIVE MODELING AND DATA MINING"

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