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: "Hyperparameter estimation in Dirichlet process mixture models"
  Abstract: In Bayesian density estimation and prediction using Dirichlet process mixtures of standard, exponential family distributions, the precision or total mass parameter of the mixing Dirichlet process is a critical hyperparame-ter that strongly influences resulting inferences about numbers of mixture components. This note shows how, with respect to a flexible class of prior distributions for this parameter, the posterior may be represented in a simple conditional form that is easily simulated. As a result, inference about this key quantity may be developed in tandem with the existing, routine Gibbs sampling algorithms for fitting such mixture models. The concept of data augmentation is important, as ever, in developing this extension of the existing algorithm. A final section notes an simple asymptotic approx imation to the posterior.

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"
    - "Bayesian curve fitting using multivariate normal mixtures"

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