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 "An Approach to Diagnosing Total Variation Convergence of MCMC Algorithms"):
  Abstract: We introduce a convergence diagnostic procedure for MCMC which operates by estimating total variation distances for the distribution of the algorithm after certain numbers of iterations. The method has advantages over many existing methods in terms of applicability, utility, computational expense and interpretability. It can be used to assess convergence of both marginal and joint posterior densities, and we show how it can be applied to the two most commonly used MCMC samplers; the Gibbs Sampler and the Metropolis Hastings algorithm. Illustrative examples highlight the utility and interpretability of the proposed diagnostic, but also highlight some of its limitations.

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 "Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models")
  Member "Assessing Convergence of Markov Chain Monte Carlo Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Convergence controls for MCMC algorithms, with applications to hidden Markov chains")

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