human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Bayesian Finite Mixtures for Nonlinear Modeling of Educational data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "EXPERIMENTING WITH THE CHEESEMAN-STUTZ EVIDENCE APPROXIMATION FOR PREDICTIVE MODELING AND DATA MINING")
  Member "Comparing Predictive Inference Methods for Discrete Domains":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Probabilistic Instance-Based Learning")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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