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: "MML mixture modelling of multi-state, Poisson, von Mises circular and Gaussian distributions"
  Abstract: Minimum Message Length (MML) is an invariant Bayesian point estimation technique which is also consistent and efficient. We provide a brief overview of MML inductive inference (Wallace and Boulton (1968), Wallace and Freeman (1987)), and how it has both an information-theoretic and a Bayesian interpretation. We then outline how MML is used for statistical parameter estimation, and how the MML mixture mod-elling program, Snob (Wallace and Boulton (1968), Wal-lace (1986), Wallace and Dowe(1994)) uses the message lengths from various parameter estimates to enable it to combine parameter estimation with selection of the number of components. The message length is (to within a constant) the logarithm of the posterior probability of the theory. So, the MML theory can also be regarded as the theory with the highest posterior probability. Snob currently assumes that variables are uncorrelated, and permits multi-variate data from Gaussian, discrete multi-state, Poisson and von Mises circular distributions.

Connected through hyperedge HE-1 (induced by source paper "BAYESIAN ESTIMATION OF THE VON MISES CONCENTRATION PARAMETER"):
  with members:
    - "MDL and MML Similarities and Differences (Introduction to Minimum Encoding Inference Part III)"

Connected through hyperedge HE-2 (induced by source paper "Finding Overlapping Distributions with MML"):
  with members:
    - "MDL and MML Similarities and Differences (Introduction to Minimum Encoding Inference Part III)"
    - "On Bayesian analysis of mixtures with an unknown number of components  Summary"
    - "Unsupervised Learning Using MML"

Connected through hyperedge HE-3 (induced by source paper "Unsupervised Learning Using MML"):
  with members:
    - "MDL and MML Similarities and Differences (Introduction to Minimum Encoding Inference Part III)"
    - "Finding Overlapping Distributions with MML"

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: Theory