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: "Self Regenerative Markov Chain Monte Carlo  Summary"
  Abstract: We propose a new method of construction of Markov chains with a given stationary distribution . This method is based on construction of an auxiliary chain with some other stationary distribution and picking elements of this auxiliary chain a suitable number of times. The proposed method has many advantages over its rivals. It is easy to implement; it provides a simple analysis; it can be faster and more efficient than the currently available techniques and it can also be adapted during the course of the simulation. We make theoretical and numerical comparisons of the characteristics of the proposed algorithm with some other MCMC techniques.

Connected through hyperedge HE-1 (induced by source paper "EXACT TRANSITION PROBABILITIES FOR THE INDEPENDENCE METROPOLIS SAMPLER"):
  with members:
    - "Rates of convergence of the Hastings and Metropolis algorithms"
    - "A note on acceptance rate criteria for CLTs for Hastings-Metropolis algorithms"

Connected through hyperedge HE-2 (induced by source paper "Adaptive proposal distribution for random walk Metropolis algorithm"):
  with members:
    - "Adaptive Markov Chain Monte Carlo through Regeneration  Summary"
    - "WEAK CONVERGENCE AND OPTIMAL SCALING OF RANDOM WALK METROPOLIS ALGORITHMS"

Connected through hyperedge HE-3 (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"
    - "Discretization of continuous Markov chains and MCMC convergence assessment"
    - "Adaptive proposal distribution for random walk Metropolis algorithm"

Connected through hyperedge HE-4 (induced by source paper "Adaptation for Self Regenerative MCMC  SUMMARY"):
  with members:
    - "Adaptive Markov Chain Monte Carlo through Regeneration  Summary"

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