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: "Discretization of continuous Markov chains and MCMC convergence assessment"
  Abstract: We show in this paper that continuous state space Markov chains can be rigorously discretized into finite Markov chains. The idea is to subsample the continuous chain at renewal times related to small sets which control the discretization. Once a finite Markov chain is derived from the MCMC output, general convergence properties on finite state spaces can be exploited for convergence assessment in several directions. Our choice is based on a divergence criterion derived from Kemeny and Snell (1960), which is first evaluated on parallel chains with a stopping time, and then implemented, more efficiently, on two parallel chains only, using Birkhoff's pointwise ergodic theorem for stopping rules. The performance of this criterion is illustrated on three standard examples.

Connected through hyperedge HE-1 (induced by source paper "Adaptive Markov Chain Monte Carlo through Regeneration  Summary"):
  with members:
    - "A simulation approach to convergence rates for Markov chain Monte Carlo algorithms"
    - "Adaptation for Self Regenerative MCMC  SUMMARY"
    - "Self Regenerative Markov Chain Monte Carlo  Summary"
    - "Adaptive proposal distribution for random walk Metropolis algorithm"

Connected through hyperedge HE-2 (induced by source paper "Looking at Markov Samplers through Cusum Path Plots: a simple diagnostic idea"):
  with members:
    - "Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review"
    - "A Gibbs Sampling Approach to Cointegration"

Connected through hyperedge HE-3 (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"
    - "Assessing Convergence of 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