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: "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.

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"
    - "Bounding Convergence Time of the Gibbs Sampler in Bayesian Image Restoration"
    - "Theoretical rates of convergence for Markov chain Monte Carlo"
    - "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 "Convergence properties of perturbed Markov chains"):
  with members:
    - "A simulation approach to convergence rates for Markov chain Monte Carlo algorithms"
    - "Analysis of the Gibbs sampler for a model related to James-Stein estimators"
    - "Auxiliary Variable Methods for Markov Chain Monte Carlo with Applications"
    - "Markov Chain Monte Carlo Methods Based on `Slicing' the Density Function"
    - "A note on convergence rates of Gibbs sampling for nonparametric mixtures"

Connected through hyperedge HE-3 (induced by source paper "On Convergence of the EM Algorithm and the Gibbs Sampler  SUMMARY"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian"
    - "On Convergence Properties of the EM Algorithm for Gaussian Mixtures"
    - "Non-linear Models for Time Series Using Mixtures of Experts"
    - "On Computing the Largest Fraction of Missing Information for the EM Algorithm and the Worst"
    - "Backfitting in Smoothing Spline ANOVA"
    - "On the Connection Between Stochastic Smoothing, Filtering and Estimation with Incomplete Data"
    - "Identifiability, Improper Priors and Gibbs Sampling for Generalized Linear Models"

Connected through hyperedge HE-4 (induced by source paper "Rates of convergence of the Hastings and Metropolis algorithms"):
  with members:
    - "Bounding Convergence Time of the Gibbs Sampler in Bayesian Image Restoration"
    - "Theoretical rates of convergence for Markov chain Monte Carlo"
    - "A simulation approach to convergence rates for Markov chain Monte Carlo algorithms"
    - "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-5 (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"
    - "An Approach to Diagnosing Total Variation Convergence of MCMC Algorithms"
    - "MCMC CONVERGENCE DIAGNOSTIC VIA THE CENTRAL LIMIT THEOREM"

Connected through hyperedge HE-6 (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"
    - "Bounding Convergence Time of the Gibbs Sampler in Bayesian Image Restoration"
    - "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"

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