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: "Assessing Convergence of Markov Chain Monte Carlo Algorithms"
  Abstract: We motivate the use of convergence diagnostic techniques for Markov Chain Monte Carlo algorithms and review various methods proposed in the MCMC literature. A common notation is established and each method is discussed with particular emphasis on implementational issues and possible extensions. The methods are compared in terms of their interpretability and applicability and recommendations are provided for particular classes of problems.

Connected through hyperedge HE-1 (induced by source paper "An Approach to Diagnosing Total Variation Convergence of MCMC Algorithms"):
  with members:
    - "BAYESIAN STATISTICS 6, pp. 000--000  Exact sampling for Bayesian inference: towards general purpose algorithms"

Connected through hyperedge HE-2 (induced by source paper "Convergence controls for MCMC algorithms, with applications to hidden Markov chains"):
  with members:
    - "Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review"
    - "MCMC CONVERGENCE DIAGNOSTIC VIA THE CENTRAL LIMIT THEOREM"

Connected through hyperedge HE-3 (induced by source paper "Rate of Convergence of the Gibbs Sampler by Gaussian Approximation  SUMMARY"):
  with members:
    - "Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review"
    - "A simulation approach to convergence rates for Markov chain Monte Carlo algorithms"
    - "On Convergence of the EM Algorithm and the Gibbs Sampler  SUMMARY"
    - "Convergence properties of perturbed Markov chains"
    - "Rates of convergence of the Hastings and Metropolis algorithms"

Connected through hyperedge HE-4 (induced by source paper "Decision Analysis by Augmented Probability Simulation"):
  with members:
    - "Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review"

Connected through hyperedge HE-5 (induced by source paper "Bounding Convergence Time of the Gibbs Sampler in Bayesian Image Restoration"):
  with members:
    - "Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review"
    - "A simulation approach to convergence rates for Markov chain Monte Carlo algorithms"
    - "Rates of convergence of the Hastings and Metropolis algorithms"

Connected through hyperedge HE-6 (induced by source paper "MCMC CONVERGENCE DIAGNOSTIC VIA THE CENTRAL LIMIT THEOREM"):
  with members:
    - "Convergence controls for MCMC algorithms, with applications to hidden Markov chains"
    - "Discretization of continuous Markov chains and MCMC convergence assessment"

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