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 "On Bayesian analysis of mixtures with an unknown number of components  Summary"):
  Abstract: New methodology for fully Bayesian mixture analysis is developed, making use of reversible jump Markov chain Monte Carlo methods, that are capable of jumping between the parameter subspaces corresponding to different numbers of components in the mixture. A sample from the full joint distribution of all unknown variables is thereby generated, and this can be used as a basis for a thorough presentation of many aspects of the posterior distribution. The methodology is applied here to the analysis of univariate normal mixtures, using a hierarchical prior model that offers an approach to dealing with weak prior information while avoiding the mathematical pitfalls of using improper priors in the mixture context.

Includes members:
  Member "BAYESIAN STATISTICS 6, pp. 000--000  Exact sampling for Bayesian inference: towards general purpose algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "An Approach to Diagnosing Total Variation Convergence of MCMC Algorithms")
  Member "FLEXIBLE PARAMETRIC MEASUREMENT ERROR MODELS":
    also connected through other hyperedges:
      (none)
  Member "Reparameterisation Issues in Mixture Modelling and their bearing on MCMC algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Bayesian curve fitting using multivariate normal mixtures")
  Member "Bayesian MARS":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Automatic Smoothing Spline Projection Pursuit  Automatic Smoothing Spline Projection Pursuit.")
  Member "Decomposable graphical Gaussian model determination":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear Models")
  Member "Finding Overlapping Distributions with MML":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Unsupervised Learning Using 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