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: "FROM METROPOLIS TO DIFFUSIONS: GIBBS STATES AND OPTIMAL SCALING"
  Abstract: This paper investigates the behaviour of the random walk Metropolis algorithm in high dimensional problems. Here we concentrate on the case where the components in the target density is a spatially homogeneous Gibbs distribution with finite range. The performance of the algorithm is strongly linked to the presence or absence of phase transition for the Gibbs distribution; the convergence time being approximately linear in dimension for problems where phase transition is not present. Related to this, there is an optimal way to scale the variance of the proposal distribution in order to maximise the speed of convergence of the algorithm. This turns out to involve scaling the variance of the proposal as the reciprocal of dimension (at least in the phase transition free case). Moreover the actual optimal scaling can be characterised in terms of the overall acceptance rate of the algorithm, the maximising value being 0:234, the value as predicted by studies on simpler classes of target density. The results are proved in the framework of a weak convergence result, which shows that the algorithm actually behaves like an infinite dimensional diffusion process in high dimensions. 1. Introduction and discussion of results

Connected through hyperedge HE-1 (induced by source paper "WEAK CONVERGENCE AND OPTIMAL SCALING OF RANDOM WALK METROPOLIS ALGORITHMS"):
  with members:
    - "Rates of convergence of the Hastings and Metropolis algorithms"
    - "Adaptive proposal distribution for random walk Metropolis algorithm"

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