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: "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.

Connected through hyperedge HE-1 (induced by source paper "BAYESIAN STATISTICS 6, pp. 000--000  Exact sampling for Bayesian inference: towards general purpose algorithms"):
  with members:
    - "Applications and extensions of MCMC in IRT: Multiple item types, missing data, and rated responses"
    - "Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models"
    - "On Bayesian analysis of mixtures with an unknown number of components  Summary"

Connected through hyperedge HE-2 (induced by source paper "Assessing Convergence of Markov Chain Monte Carlo Algorithms"):
  with members:
    - "Decision Analysis by Augmented Probability Simulation"
    - "Bounding Convergence Time of the Gibbs Sampler in Bayesian Image Restoration"
    - "Convergence controls for MCMC algorithms, with applications to hidden Markov chains"
    - "Rate of Convergence of the Gibbs Sampler by Gaussian Approximation  SUMMARY"
    - "MCMC CONVERGENCE DIAGNOSTIC VIA THE CENTRAL LIMIT THEOREM"

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