human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Geometric and Subgeometric Convergence of Diffusions with Given Stationary Distributions, and Their Discretizations"):
  Abstract: We describe algorithms for estimating a given measure known up to a constant of proportionality, based on a large class of diffusions (extending the Langevin model) for which is invariant. We show that under weak conditions one can choose from this class in such a way that the diffusions converge at exponential rate to , and one can even ensure that convergence is independent of the starting point of the algorithm. When convergence is less than exponential we show that it is often polynomial at known rates. We then consider methods of discretizing the diffusion in time, and find methods which inherit the convergence rates of the continuous time process. These contrast with the behaviour of the naive or Euler discretization, which can behave badly even in simple cases.

Includes members:
  Member "Rates of convergence of the Hastings and Metropolis algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "WEAK CONVERGENCE AND OPTIMAL SCALING OF RANDOM WALK METROPOLIS ALGORITHMS")
  Member "Exponential Convergence of Langevin Diffusions and Their Discrete Approximations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Rates of convergence of the Hastings and Metropolis algorithms")
  Member "Self-Targeting Candidates for Metropolis-Hastings Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Exponential Convergence of Langevin Diffusions and Their Discrete Approximations")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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