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 "Bayesian curve fitting using multivariate normal mixtures"):
  Abstract: Problems of regression smoothing and curve fitting are addressed via predictive inference in a flexible class of mixture models. Multi-dimensional density estimation using Dirichlet mixture models provides the theoretical basis for semi-parametric regression methods in which fitted regression functions may be deduced as means of conditional predictive distributions. These Bayesian regression functions have features similar to generalised kernel regression estimates, but the formal analysis addresses problems of multivariate smoothing parameter estimation and the assessment of uncertainties about regression functions naturally. Computations are based on multi-dimensional versions of existing Markov chain simulation analysis of univariate Dirichlet mixture models.

Includes members:
  Member "Reparameterisation Issues in Mixture Modelling and their bearing on MCMC algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "On Bayesian analysis of mixtures with an unknown number of components  Summary")
  Member "Computing Nonparametric Hierarchical Models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Hyperparameter estimation in Dirichlet process mixture models")

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