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: "Self-Targeting Candidates for Metropolis-Hastings Algorithms"
  Abstract: The Metropolis-Hastings algorithm for estimating a distribution is based on choosing a candidate Markov chain and then accepting or rejecting moves of the candidate to produce a chain known to have as the invariant measure. The traditional methods use candidates essentially unconnected to . Based on diffusions for which is invariant, we develop for one-dimensional distributions a class of candidate distributions that "self-target" towards the high density areas of . These produce Metropolis-Hastings algorithms with convergence rates that appear to be considerably better than those known for the traditional candidate choices, such as random walk. In particular, for wide classes of these choices may effectively help reduce the "burn-in" problem. We illustrate this behaviour for examples with exponential and polynomial tails, and for a logistic regression model using a Gibbs sampling algorithm.

Connected through hyperedge HE-1 (induced by source paper "Exponential Convergence of Langevin Diffusions and Their Discrete Approximations"):
  with members:
    - "Rates of convergence of the Hastings and Metropolis algorithms"
    - "A note on acceptance rate criteria for CLTs for Hastings-Metropolis algorithms"
    - "Geometric and Subgeometric Convergence of Diffusions with Given Stationary Distributions, and Their Discretizations"

Connected through hyperedge HE-2 (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-3 (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"
    - "Exponential Convergence of Langevin Diffusions and Their Discrete Approximations"

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