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 "Decomposable graphical Gaussian model determination"):
  Abstract: We propose a methodology for Bayesian model determination in decomposable graphical Gaussian models. To achieve this aim we consider a hyper inverse Wishart prior distribution on the concentration matrix for each given graph. To ensure compatibility across models, such prior distributions are obtained by marginalisation from the prior conditional on the complete graph. We explore alternative structures for the hyperparameters of the latter, and their consequences for the model. Model determination is carried out by implementing a reversible jump MCMC sampler. In particular, the dimension-changing move we propose involves adding or dropping an edge from the graph. We characterise the set of moves which preserve the decomposability of the graph, giving a fast algorithm for maintaining the junction tree representation of the graph at each sweep. As state variable, we propose to use the incomplete variance-covariance matrix, containing only the elements for which the corresponding element of the inverse is nonzero. This allows all computations to be performed locally, at the clique level, which is a clear advantage for the analysis of large and complex data-sets. Finally, the statistical and computational performance of the procedure is illustrated by means of both artificial and real multidimensional data-sets.

Includes members:
  Member "Model Selection and Accounting for Model Uncertainty in Linear Regression Models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Bayesian inference for nondecomposable graphical Gaussian models")
  Member "Bayesian Graphical Models for Discrete Data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models")
  Member "Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear Models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models")
  Member "On Bayesian analysis of mixtures with an unknown number of components  Summary":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Finding Overlapping Distributions with MML")

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