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: "Bounding Convergence Time of the Gibbs Sampler in Bayesian Image Restoration"
  Abstract: This paper gives precise, easy to compute bounds on the convergence time of the Gibbs sampler used in Bayesian image reconstruction. For sampling from the Gibbs distribution both with and without the presence of an external field, bounds that are N 2 in the number of pixels are obtained, with a proportionality constant that is easy to calculate. Some key words: Bayesian image restoration; Convergence; Gibbs sampler; Ising model; Markov chain Monte Carlo.

Connected through hyperedge HE-1 (induced by source paper "A simulation approach to convergence rates for Markov chain Monte Carlo algorithms"):
  with members:
    - "Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review"
    - "Theoretical rates of convergence for Markov chain Monte Carlo"
    - "Rate of Convergence of the Gibbs Sampler by Gaussian Approximation  SUMMARY"
    - "Analysis of the Gibbs sampler for a model related to James-Stein estimators"
    - "Convergence properties of perturbed Markov chains"
    - "Rates of convergence of the Hastings and Metropolis algorithms"
    - "A note on convergence rates of Gibbs sampling for nonparametric mixtures"
    - "Adaptive Markov Chain Monte Carlo through Regeneration  Summary"

Connected through hyperedge HE-2 (induced by source paper "Rates of convergence of the Hastings and Metropolis algorithms"):
  with members:
    - "Theoretical rates of convergence for Markov chain Monte Carlo"
    - "A simulation approach to convergence rates for Markov chain Monte Carlo algorithms"
    - "Rate of Convergence of the Gibbs Sampler by Gaussian Approximation  SUMMARY"
    - "Analysis of the Gibbs sampler for a model related to James-Stein estimators"
    - "EXACT TRANSITION PROBABILITIES FOR THE INDEPENDENCE METROPOLIS SAMPLER"
    - "Estimating L 1 Error of Kernel Estimator: Monitoring Convergence of Markov Samplers"
    - "A note on acceptance rate criteria for CLTs for Hastings-Metropolis algorithms"
    - "Exponential Convergence of Langevin Diffusions and Their Discrete Approximations"

Connected through hyperedge HE-3 (induced by source paper "Assessing Convergence of Markov Chain Monte Carlo Algorithms"):
  with members:
    - "Decision Analysis by Augmented Probability Simulation"
    - "Convergence controls for MCMC algorithms, with applications to hidden Markov chains"
    - "Rate of Convergence of the Gibbs Sampler by Gaussian Approximation  SUMMARY"
    - "An Approach to Diagnosing Total Variation Convergence of MCMC Algorithms"
    - "MCMC CONVERGENCE DIAGNOSTIC VIA THE CENTRAL LIMIT THEOREM"

Connected through hyperedge HE-4 (induced by source paper "Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review"):
  with members:
    - "Applications and extensions of MCMC in IRT: Multiple item types, missing data, and rated responses"
    - "Decision Analysis by Augmented Probability Simulation"
    - "Perfect Simulation in Stochastic Geometry"
    - "Theoretical rates of convergence for Markov chain Monte Carlo"
    - "Maximum Working Likelihood Inference with Markov Chain Monte Carlo"
    - "Estimating Ratios of Normalizing Constants for Densities with Different Dimensions"
    - "Markov Chain Monte Carlo in Practice: A Roundtable Discussion"
    - "A simulation approach to convergence rates for Markov chain Monte Carlo algorithms"

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