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 "Rate of Convergence of the Gibbs Sampler by Gaussian Approximation  SUMMARY"):
  Abstract: In this article we approximate the rate of convergence of the Gibbs sampler by a normal approximation of the target distribution. Based on this approximation, we consider many implementational issues for the Gibbs sampler, e.g., updating strategy, parameterization and blocking. We give theoretical results to justify our approximation and illustrate our methods in a number of realistic examples.

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 "A simulation approach to convergence rates for Markov chain Monte Carlo algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Convergence properties of perturbed Markov chains")
  Member "On Convergence of the EM Algorithm and the Gibbs Sampler  SUMMARY":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Non-linear Models for Time Series Using Mixtures of Experts")
  Member "Convergence properties of perturbed Markov chains":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Markov Chain Monte Carlo Methods Based on `Slicing' the Density Function")
  Member "Assessing Convergence of Markov Chain Monte Carlo Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "An Approach to Diagnosing Total Variation Convergence of MCMC Algorithms")
  Member "Rates of convergence of the Hastings and Metropolis algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "WEAK CONVERGENCE AND OPTIMAL SCALING OF RANDOM WALK METROPOLIS ALGORITHMS")

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