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: "Adaptation for Self Regenerative MCMC  SUMMARY"
  Abstract: The self regenerative MCMC is a tool for constructing a Markov chain with a given stationary distribution by constructing an auxiliary chain with some other stationary distribution . Elements of the auxiliary chain are picked a suitable random number of times so that the resulting chain has the stationary distribution , Sahu and Zhigljavsky (1998). In this article we provide a generic adaptation scheme for the above algorithm. The adaptive scheme is to use the knowledge of the stationary distribution gathered so far and then to update during the course of the simulation. This method is easy to implement and often leads to considerable improvement. We obtain theoretical results for the adaptive scheme. Our proposed methodology is illustrated with a number of realistic examples in Bayesian computation and its performance is compared with other available MCMC techniques. In one of our applications we develop a non-linear dynamics model for modeling predator-prey relationships in the wild.

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"
    - "Self Regenerative Markov Chain Monte Carlo  Summary"
    - "Discretization of continuous Markov chains and MCMC convergence assessment"
    - "Adaptive proposal distribution for random walk Metropolis algorithm"

Connected through hyperedge HE-2 (induced by source paper "Self Regenerative Markov Chain Monte Carlo  Summary"):
  with members:
    - "Adaptive Markov Chain Monte Carlo through Regeneration  Summary"
    - "Adaptive proposal distribution for random walk Metropolis algorithm"
    - "EXACT TRANSITION PROBABILITIES FOR THE INDEPENDENCE METROPOLIS SAMPLER"

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