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: "Looking at Markov Samplers through Cusum Path Plots: a simple diagnostic idea"
  Abstract: In this paper, we propose to monitor a Markov chain sampler using the cusum path plot of a chosen 1-dimensional summary statistic. We argue that the cusum path plot can bring out, more effectively than the sequential plot, those aspects of a Markov sampler which tell the user how quickly or slowly the sampler is moving around in its sample space, in the direction of the summary statistic. The proposal is then illustrated in four examples which represent situations where the cusum path plot works well and not well. Moreover, a rigorous analysis is given for one of the examples. We conclude that the cusum path plot is an effective tool for convergence diagnostics of a Markov sampler and for comparing different Markov samplers.

Connected through hyperedge HE-1 (induced by source paper "Discretization of continuous Markov chains and MCMC convergence assessment"):
  with members:
    - "Adaptive Markov Chain Monte Carlo through Regeneration  Summary"
    - "MCMC CONVERGENCE DIAGNOSTIC VIA THE CENTRAL LIMIT THEOREM"

Connected through hyperedge HE-2 (induced by source paper "A Gibbs Sampling Approach to Cointegration"):
  with members:
    (none)

Connected through hyperedge HE-3 (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