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: "Importance Sampling"
  Abstract: Technical Report No. 9805, Department of Statistics, University of Toronto Abstract. Simulated annealing | moving from a tractable distribution to a distribution of interest via a sequence of intermediate distributions | has traditionally been used as an inexact method of handling isolated modes in Markov chain samplers. Here, it is shown how one can use the Markov chain transitions for such an annealing sequence to define an importance sampler. The Markov chain aspect allows this method to perform acceptably even for high-dimensional problems, where finding good importance sampling distributions would otherwise be very difficult, while the use of importance weights ensures that the estimates found converge to the correct values as the number of annealing runs increases. This annealed importance sampling procedure resembles the second half of the previously-studied tempered transitions, and can be seen as a generalization of a recently-proposed variant of sequential importance sampling. It is also related to thermodynamic integration methods for estimating ratios of normalizing constants. Annealed importance sampling is most attractive when isolated modes are present, or when estimates of normalizing constants are required, but it may also be more generally useful, since its independent sampling allows one to bypass some of the problems of assessing convergence and autocorrelation in Markov chain samplers.

Connected through hyperedge HE-1 (induced by source paper "Sequential Importance Sampling for Nonparametric Bayes Models: The Next Generation Running"):
  with members:
    - "Importance Sampling"

Connected through hyperedge HE-2 (induced by source paper "Sampling from Multimodal Distributions Using Tempered Transitions"):
  with members:
    - "Maximum Working Likelihood Inference with Markov Chain Monte Carlo"
    - "Markov Chain Monte Carlo in Practice: A Roundtable Discussion"
    - "Importance Sampling"

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