human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Finding Overlapping Distributions with MML":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Unsupervised Learning Using MML")
  Member "Unsupervised Learning Using MML":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Finding Overlapping Distributions with MML")
  Member "BAYESIAN ESTIMATION OF THE VON MISES CONCENTRATION PARAMETER":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "MDL and MML Similarities and Differences (Introduction to Minimum Encoding Inference Part III)")
  Member "SINGLE FACTOR ANALYSIS BY MML ESTIMATION":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "MDL and MML Similarities and Differences (Introduction to Minimum Encoding Inference Part III)")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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