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: "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.

Connected through hyperedge HE-1 (induced by source paper "Computing Nonparametric Hierarchical Models"):
  with members:
    - "Studies of Neurological Transmission Analysis using Hierarchical Bayesian Mixture Models"
    - "Practical Bayesian Inference Using Mixtures of Mixtures"
    - "Hyperparameter estimation in Dirichlet process mixture models"

Connected through hyperedge HE-2 (induced by source paper "Reparameterisation Issues in Mixture Modelling and their bearing on MCMC algorithms"):
  with members:
    - "On Bayesian analysis of mixtures with an unknown number of components  Summary"

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