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: "Analysis of a Non-Reversible Markov Chain Sampler"
  Abstract: Technical Report BU-1385-M, Biometrics Unit, Cornell University Abstract We analyse the convergence to stationarity of a simple non-reversible Markov chain that serves as a model for several non-reversible Markov chain sampling methods that are used in practice. Our theoretical and numerical results show that non-reversibility can indeed lead to improvements over the diffusive behavior of simple Markov chain sampling schemes. The analysis uses both probabilistic techniques and an explicit diagonalisation. We thank David Aldous, Martin Hildebrand, Brad Mann, and Laurent Saloff-Coste for their help.

Connected through hyperedge HE-1 (induced by source paper "Markov Chain Monte Carlo Methods Based on `Slicing' the Density Function"):
  with members:
    - "Continuous sigmoidal belief networks trained using slice sampling"
    - "Convergence properties of perturbed Markov chains"
    - "Auxiliary Variable Methods for Markov Chain Monte Carlo with Applications"
    - "SUPPRESSING RANDOM WALKS IN MARKOV CHAIN MONTE CARLO USING ORDERED OVERRELAXATION"

Connected through hyperedge HE-2 (induced by source paper "SUPPRESSING RANDOM WALKS IN MARKOV CHAIN MONTE CARLO USING ORDERED OVERRELAXATION"):
  with members:
    - "Markov Chain Monte Carlo Methods Based on `Slicing' the Density Function"

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