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: "Computing Nonparametric Hierarchical Models"
  Abstract: Bayesian models involving Dirichlet process mixtures are at the heart of the modern nonparametric Bayesian movement. Much of the rapid development of these models in the last decade has been a direct result of advances in simulation-based computational methods. Some of the very early work in this area, circa 1988-1991, focused on the use of such nonparametric ideas and models in applications of otherwise standard hierarchical models. This chapter provides some historical review and perspective on these developments, with a prime focus on the use and integration of such nonparametric ideas in hierarchical models. We illustrate the ease with which the strict parametric assumptions common to most standard Bayesian hierarchical models can be relaxed to incorporate uncertainties about functional forms using Dirichlet process components, partly enabled by the approach to computation using MCMC methods. The resulting methology is illustrated with two examples taken from an unpublished 1992 report on the topic.

Connected through hyperedge HE-1 (induced by source paper "Hyperparameter estimation in Dirichlet process mixture models"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Studies of Neurological Transmission Analysis using Hierarchical Bayesian Mixture Models"):
  with members:
    - "Priors and Component Structures in Autoregressive Time Series Models"
    - "Mixture Models in the Exploration of Structure-Activity Relationships in Drug Design"
    - "Practical Bayesian Inference Using Mixtures of Mixtures"

Connected through hyperedge HE-3 (induced by source paper "Practical Bayesian Inference Using Mixtures of Mixtures"):
  with members:
    - "Studies of Neurological Transmission Analysis using Hierarchical Bayesian Mixture Models"
    - "COMPUTING DISTRIBUTIONS OF ORDER STATISTICS"

Connected through hyperedge HE-4 (induced by source paper "Bayesian curve fitting using multivariate normal mixtures"):
  with members:
    - "Reparameterisation Issues in Mixture Modelling and their bearing on MCMC algorithms"

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