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: "On Bayesian analysis of mixtures with an unknown number of components  Summary"
  Abstract: New methodology for fully Bayesian mixture analysis is developed, making use of reversible jump Markov chain Monte Carlo methods, that are capable of jumping between the parameter subspaces corresponding to different numbers of components in the mixture. A sample from the full joint distribution of all unknown variables is thereby generated, and this can be used as a basis for a thorough presentation of many aspects of the posterior distribution. The methodology is applied here to the analysis of univariate normal mixtures, using a hierarchical prior model that offers an approach to dealing with weak prior information while avoiding the mathematical pitfalls of using improper priors in the mixture context.

Connected through hyperedge HE-1 (induced by source paper "Finding Overlapping Distributions with MML"):
  with members:
    - "MDL and MML Similarities and Differences (Introduction to Minimum Encoding Inference Part III)"
    - "MML mixture modelling of multi-state, Poisson, von Mises circular and Gaussian distributions"
    - "Unsupervised Learning Using MML"

Connected through hyperedge HE-2 (induced by source paper "FLEXIBLE PARAMETRIC MEASUREMENT ERROR MODELS"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "BAYESIAN STATISTICS 6, pp. 000--000  Exact sampling for Bayesian inference: towards general purpose algorithms"):
  with members:
    - "Applications and extensions of MCMC in IRT: Multiple item types, missing data, and rated responses"
    - "Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models"
    - "An Approach to Diagnosing Total Variation Convergence of MCMC Algorithms"

Connected through hyperedge HE-4 (induced by source paper "Decomposable graphical Gaussian model determination"):
  with members:
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"
    - "Bayesian Graphical Models for Discrete Data"
    - "Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear Models"

Connected through hyperedge HE-5 (induced by source paper "Bayesian MARS"):
  with members:
    - "Automatic Smoothing Spline Projection Pursuit  Automatic Smoothing Spline Projection Pursuit."

Connected through hyperedge HE-6 (induced by source paper "Reparameterisation Issues in Mixture Modelling and their bearing on MCMC algorithms"):
  with members:
    - "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