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 "Convergence controls for MCMC algorithms, with applications to hidden Markov chains"):
  Abstract: In complex models like hidden Markov chains, the convergence of the MCMC algorithms used to approximate the posterior distribution and the Bayes estimates of the parameters of interest must be controlled in a robust manner. We propose in this paper a series of on-line controls, which rely on classical non-parametric tests, to evaluate independence from the start-up distribution, stability of the Markov chain, and asymptotic normality. These tests lead to graphical control spreadsheets which are presented in the set-up of normal mixture hidden Markov chains to compare the full Gibbs sampler with an aggregated Gibbs sampler based on the forward-backward formulae.

Includes members:
  Member "Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Perfect Simulation in Stochastic Geometry")
  Member "Assessing Convergence of Markov Chain Monte Carlo Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "An Approach to Diagnosing Total Variation Convergence of MCMC Algorithms")
  Member "MCMC CONVERGENCE DIAGNOSTIC VIA THE CENTRAL LIMIT THEOREM":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Discretization of continuous Markov chains and MCMC convergence assessment")

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