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: "Reparameterisation Issues in Mixture Modelling and their bearing on MCMC algorithms"
  Abstract: There is increasing need for efficient estimation of mixture distributions, especially following the explosion in the use of these as modelling tools in many applied fields. We propose in this paper a Bayesian noninformative approach for the estimation of normal mixtures which relies on a reparameterisation of the secondary components of the mixture in terms of divergence from the main component. As well as providing an intuitively appealing representation at the modelling stage, this reparameterisation has important bearing on both the prior distribution and the performance of MCMC algorithms. We compare two possible reparameterisations extending Mengersen and Robert (1996) and show that the reparameterisation which does not link the secondary components together is associated with poor convergence properties of MCMC algorithms.

Connected through hyperedge HE-1 (induced by source paper "Bayesian curve fitting using multivariate normal mixtures"):
  with members:
    - "Computing Nonparametric Hierarchical Models"

Connected through hyperedge HE-2 (induced by source paper "On Bayesian analysis of mixtures with an unknown number of components  Summary"):
  with members:
    - "BAYESIAN STATISTICS 6, pp. 000--000  Exact sampling for Bayesian inference: towards general purpose algorithms"
    - "FLEXIBLE PARAMETRIC MEASUREMENT ERROR MODELS"
    - "Bayesian MARS"
    - "Decomposable graphical Gaussian model determination"
    - "Finding Overlapping Distributions with MML"

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