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: "Exponential Convergence of Langevin Diffusions and Their Discrete Approximations"
  Abstract: In this paper we consider a continous time method of approximating a given distribution using the Langevin diffusion dL t = dW t + 1 2 r log (L t )dt: We find conditions under which this diffusion converges exponentially quickly to or does not: in one dimension, these are essentially that for distributions with exponential tails of the form (x) / exp(fljxj fi ), 0 &lt; fi &lt; 1, exponential convergence occurs if and only if fi 1. We then consider conditions under which the discrete approximations to the diffusion converge. We first show that even when the diffusion itself converges, naive discretisations need not do so. We then consider a "Metropolis-adjusted" version of the algorithm, and find conditions under which this also converges at an exponential rate: perhaps surprisingly, even the Metropolised version need not converge exponentially fast even if the diffusion does. We briefly discuss a truncated form of the algorithm which, in practice, should avoid the difficulties of the other forms.

Connected through hyperedge HE-1 (induced by source paper "Rates of convergence of the Hastings and Metropolis algorithms"):
  with members:
    - "Bounding Convergence Time of the Gibbs Sampler in Bayesian Image Restoration"
    - "Theoretical rates of convergence for Markov chain Monte Carlo"
    - "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"
    - "EXACT TRANSITION PROBABILITIES FOR THE INDEPENDENCE METROPOLIS SAMPLER"
    - "Estimating L 1 Error of Kernel Estimator: Monitoring Convergence of Markov Samplers"
    - "A note on acceptance rate criteria for CLTs for Hastings-Metropolis algorithms"

Connected through hyperedge HE-2 (induced by source paper "Geometric and Subgeometric Convergence of Diffusions with Given Stationary Distributions, and Their Discretizations"):
  with members:
    - "Rates of convergence of the Hastings and Metropolis algorithms"
    - "Self-Targeting Candidates for Metropolis-Hastings Algorithms"

Connected through hyperedge HE-3 (induced by source paper "Self-Targeting Candidates for Metropolis-Hastings Algorithms"):
  with members:
    - "Rates of convergence of the Hastings and Metropolis algorithms"
    - "Geometric and Subgeometric Convergence of Diffusions with Given Stationary Distributions, and Their Discretizations"

Connected through hyperedge HE-4 (induced by source paper "A note on acceptance rate criteria for CLTs for Hastings-Metropolis algorithms"):
  with members:
    - "Rates of convergence of the Hastings and Metropolis algorithms"
    - "Geometric Ergodicity and Hybrid Markov Chains"
    - "EXACT TRANSITION PROBABILITIES FOR THE INDEPENDENCE METROPOLIS SAMPLER"

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