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: "Practical Bayesian Inference Using Mixtures of Mixtures"
  Abstract: Discrete mixtures of normal distributions are widely used in modeling amplitude fluctuations of electrical potentials at synapses of human, and other animal nervous systems. The usual framework has independent data values y j arising as y j = j + x n 0 +j where the means j come from some discrete prior G() and the unknown x n 0 +j 's and observed x j ; j = 1; : : : ; n 0 are gaussian noise terms. A practically important development of the associated statistical methods is the issue of non-normality of the noise terms, often the norm rather than the exception in the neurological context. We have recently developed models, based on convolutions of Dirichlet process mixtures, for such problems. Explicitly, we model the noise data values x j as arising from a Dirich-let process mixture of normals, in addition to modeling the location prior G() as a Dirichlet process itself. This induces a Dirichlet mixture of mixtures of normals, whose analysis may be developed using Gibbs sampling techniques. We discuss these models and their analysis, and illustrate in the context of neurological response analysis.

Connected through hyperedge HE-1 (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"
    - "Computing Nonparametric Hierarchical Models"

Connected through hyperedge HE-2 (induced by source paper "COMPUTING DISTRIBUTIONS OF ORDER STATISTICS"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "Computing Nonparametric Hierarchical Models"):
  with members:
    - "Studies of Neurological Transmission Analysis using Hierarchical Bayesian Mixture Models"
    - "Bayesian curve fitting using multivariate normal mixtures"
    - "Hyperparameter estimation in Dirichlet process mixture models"

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