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: "Adaptive proposal distribution for random walk Metropolis algorithm"
  Abstract: The choice of a suitable MCMC method and further the choice of a proposal distribution is known to be crucial for the convergence of the Markov chain. However, in many cases the choice of an effective proposal distribution is difficult. As a remedy we suggest a method called Adaptive Proposal (AP). Although the stationary distribution of the AP algorithm is slightly biased, it appears to provide an efficient tool for, e.g., reasonably low dimensional problems, as typically encountered in non-linear regression problems in natural sciences. As a realistic example we include a successful application of the AP algorithm in parameter estimation for the satellite instrument 'GO-MOS'. In this paper we also present a comprehensive test procedure and systematic performance criteria for comparing Adaptive Proposal algorithm with more traditional Metropolis algorithms.

Connected through hyperedge HE-1 (induced by source paper "WEAK CONVERGENCE AND OPTIMAL SCALING OF RANDOM WALK METROPOLIS ALGORITHMS"):
  with members:
    - "Rates of convergence of the Hastings and Metropolis algorithms"
    - "FROM METROPOLIS TO DIFFUSIONS: GIBBS STATES AND OPTIMAL SCALING"

Connected through hyperedge HE-2 (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"
    - "Discretization of continuous Markov chains and MCMC convergence assessment"

Connected through hyperedge HE-3 (induced by source paper "Self Regenerative Markov Chain Monte Carlo  Summary"):
  with members:
    - "Adaptation for Self Regenerative MCMC  SUMMARY"
    - "Adaptive Markov Chain Monte Carlo through Regeneration  Summary"
    - "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