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: "Perfect Sampling of Harris Recurrent Markov Chains"
  Abstract: We develop an algorithm for simulating "perfect" random samples from the invariant measure of a Harris recurrent Markov chain. The method uses backward coupling of embedded regeneration times, and works most effectively for finite chains and for stochastically monotone chains even on continuous spaces, where paths may be sandwiched below "upper" and "lower" processes. Examples show that more naive approaches to constructing such bounding processes may be considerably biased, but that the algorithm can be simplified in certain cases to make it easier to run. We give explicit analytic bounds on the backward coupling times in the stochastically monotone case.

Connected through hyperedge HE-1 (induced by source paper "Extensions of Fill's algorithm for perfect simulation"):
  with members:
    - "Perfect Simulation of some Point Processes for the Impatient User"
    - "An extension of Fill's exact sampling algorithm to non-monotone chains*"
    - "EXACT SIMULATION USING MARKOV CHAINS"
    - "PERFECT SIMULATION OF CONDITIONALLY SPECIFIED MODELS"

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