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: "Markov Chain Monte Carlo Methods Based on `Slicing' the Density Function"
  Abstract: Technical Report No. 9722, Department of Statistics, University of Toronto Abstract. One way to sample from a distribution is to sample uniformly from the region under the plot of its density function. A Markov chain that converges to this uniform distribution can be constructed by alternating uniform sampling in the vertical direction with uniform sampling from the horizontal `slice' defined by the current vertical position. Variations on such `slice sampling' methods can easily be implemented for univariate distributions, and can be used to sample from a multivariate distribution by updating each variable in turn. This approach is often easier to implement than Gibbs sampling, and may be more efficient than easily-constructed versions of the Metropolis algorithm. Slice sampling is therefore attractive in routine Markov chain Monte Carlo applications, and for use by software that automatically generates a Markov chain sampler from a model specification. One can also easily devise overrelaxed versions of slice sampling, which sometimes greatly improve sampling efficiency by suppressing random walk behaviour. Random walks can also be avoided in some slice sampling schemes that simultaneously update all variables.

Connected through hyperedge HE-1 (induced by source paper "Continuous sigmoidal belief networks trained using slice sampling"):
  with members:
    - "Generative Models for Discovering Sparse Distributed Representations"
    - "Discovering Structure in Continuous Variables Using Bayesian Networks"

Connected through hyperedge HE-2 (induced by source paper "Convergence properties of perturbed Markov chains"):
  with members:
    - "A simulation approach to convergence rates for Markov chain Monte Carlo algorithms"
    - "Rate of Convergence of the Gibbs Sampler by Gaussian Approximation  SUMMARY"
    - "Analysis of the Gibbs sampler for a model related to James-Stein estimators"
    - "Auxiliary Variable Methods for Markov Chain Monte Carlo with Applications"
    - "A note on convergence rates of Gibbs sampling for nonparametric mixtures"

Connected through hyperedge HE-3 (induced by source paper "Analysis of a Non-Reversible Markov Chain Sampler"):
  with members:
    - "SUPPRESSING RANDOM WALKS IN MARKOV CHAIN MONTE CARLO USING ORDERED OVERRELAXATION"

Connected through hyperedge HE-4 (induced by source paper "SUPPRESSING RANDOM WALKS IN MARKOV CHAIN MONTE CARLO USING ORDERED OVERRELAXATION"):
  with members:
    - "Analysis of a Non-Reversible Markov Chain Sampler"

Connected through hyperedge HE-5 (induced by source paper "Auxiliary Variable Methods for Markov Chain Monte Carlo with Applications"):
  with members:
    - "Convergence properties of perturbed Markov chains"
    - "A note on convergence rates of Gibbs sampling for nonparametric mixtures"

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