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 "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.

Includes members:
  Member "Looking at Markov Samplers through Cusum Path Plots: a simple diagnostic idea":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "A Gibbs Sampling Approach to Cointegration")
  Member "Adaptive Markov Chain Monte Carlo through Regeneration  Summary":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A simulation approach to convergence rates for Markov chain Monte Carlo algorithms")
  Member "MCMC CONVERGENCE DIAGNOSTIC VIA THE CENTRAL LIMIT THEOREM":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Convergence controls for MCMC algorithms, with applications to hidden Markov chains")

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