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: "Developments in Probabilistic Modelling with Neural Networks|Ensemble Learning"
  Abstract: In this paper I give a review of ensemble learning using a simple example.

Connected through hyperedge HE-1 (induced by source paper "Free Energy Minimization Algorithm for Decoding and Cryptanalysis three binary vectors: s of length N"):
  with members:
    - "Ensemble Learning and Evidence Maximization"

Connected through hyperedge HE-2 (induced by source paper "Ensemble Learning for Hidden Markov Models"):
  with members:
    - "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"
    - "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights"
    - "Choice of Basis for Laplace Approximation"

Connected through hyperedge HE-3 (induced by source paper "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights"):
  with members:
    - "FLAT MINIMA Neural Computation 9(1):1-42 (1997)"
    - "Ensemble Learning and Evidence Maximization"
    - "Ensemble Learning for Hidden Markov Models"
    - "Probabilistic Networks: New Models and New Methods"
    - "A Practical Bayesian Framework for Backprop Networks"

Connected through hyperedge HE-4 (induced by source paper "Ensemble Learning and Evidence Maximization"):
  with members:
    - "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"
    - "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights"
    - "A Practical Bayesian Framework for Backprop Networks"
    - "Free Energy Minimization Algorithm for Decoding and Cryptanalysis three binary vectors: s of length N"

Connected through hyperedge HE-5 (induced by source paper "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"):
  with members:
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "A HIERARCHICAL COMMUNITY OF EXPERTS"
    - "A Statistical Approach to Decision Tree Modeling"
    - "On Convergence Properties of the EM Algorithm for Gaussian Mixtures"
    - "Efficient Stochastic Source Coding and an Application to a Bayesian Network Source Model"
    - "On Structured Variational Approximations"
    - "The Expectation-Maximization Algorithm for MAP Estimation"
    - "Localist Attractor Networks"

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