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: "Finding Overlapping Distributions with MML"
  Abstract: This paper considers an aspect of mixture modelling. Significantly overlapping distributions require more data for their parameters to be accurately estimated than well separated distributions. For example, two Gaussian distributions are considered to significantly overlap when their means are within three standard deviations of each other. If insufficient data is available, only a single component distribution will be estimated, although the data originates from two component distributions. We consider how much data is required to distinguish two component distributions from one distribution in mixture modelling using the minimum message length (MML) criterion. First, we perform experiments which show the MML criterion performs well relative to other Bayesian criteria. Second, we make two improvements to the existing MML estimates, that improve its performance with overlapping distributions.

Connected through hyperedge HE-1 (induced by source paper "Unsupervised Learning Using 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"

Connected through hyperedge HE-2 (induced by source paper "MML mixture modelling of multi-state, Poisson, von Mises circular and Gaussian distributions"):
  with members:
    - "Unsupervised Learning Using MML"
    - "BAYESIAN ESTIMATION OF THE VON MISES CONCENTRATION PARAMETER"
    - "SINGLE FACTOR ANALYSIS BY MML ESTIMATION"

Connected through hyperedge HE-3 (induced by source paper "MDL and MML Similarities and Differences (Introduction to Minimum Encoding Inference Part III)"):
  with members:
    - "Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models"
    - "A Practical Bayesian Framework for Backprop Networks"
    - "On Pruning and Averaging Decision Trees"
    - "Unsupervised Learning Using MML"
    - "BAYESIAN ESTIMATION OF THE VON MISES CONCENTRATION PARAMETER"
    - "SINGLE FACTOR ANALYSIS BY MML ESTIMATION"
    - "Bayesian and Information-Theoretic Priors for Bayesian Network Parameters"
    - "Stochastic Complexity Based Estimation of Missing Elements in Questionnaire Data"

Connected through hyperedge HE-4 (induced by source paper "On Bayesian analysis of mixtures with an unknown number of components  Summary"):
  with members:
    - "BAYESIAN STATISTICS 6, pp. 000--000  Exact sampling for Bayesian inference: towards general purpose algorithms"
    - "FLEXIBLE PARAMETRIC MEASUREMENT ERROR MODELS"
    - "Reparameterisation Issues in Mixture Modelling and their bearing on MCMC algorithms"
    - "Bayesian MARS"
    - "Decomposable graphical Gaussian model determination"

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