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: "Sampling from Multimodal Distributions Using Tempered Transitions"
  Abstract: Technical Report No. 9421, Department of Statistics, University of Toronto Abstract. I present a new Markov chain sampling method appropriate for distributions with isolated modes. Like the recently-developed method of "simulated tempering", the "tempered transition" method uses a series of distributions that interpolate between the distribution of interest and a distribution for which sampling is easier. The new method has the advantage that it does not require approximate values for the normalizing constants of these distributions, which are needed for simulated tempering, and can be tedious to estimate. Simulated tempering performs a random walk along the series of distributions used. In contrast, the tempered transitions of the new method move systematically from the desired distribution, to the easily-sampled distribution, and back to the desired distribution. This systematic movement avoids the inefficiency of a random walk, an advantage that unfortunately is cancelled by an increase in the number of interpolating distributions required. Because of this, the sampling efficiency of the tempered transition method in simple problems is similar to that of simulated tempering. On more complex distributions, however, simulated tempering and tempered transitions may perform differently. Which is better depends on the ways in which the interpolating distributions are "deceptive".

Connected through hyperedge HE-1 (induced by source paper "Markov Chain Monte Carlo in Practice: A Roundtable Discussion"):
  with members:
    - "Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review"
    - "On MCMC Sampling in Hierarchical Longitudinal Models  SUMMARY"

Connected through hyperedge HE-2 (induced by source paper "Maximum Working Likelihood Inference with Markov Chain Monte Carlo"):
  with members:
    - "Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review"

Connected through hyperedge HE-3 (induced by source paper "Importance Sampling"):
  with members:
    - "Sequential Importance Sampling for Nonparametric Bayes Models: The Next Generation Running"

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