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: "Ensemble Learning and Evidence Maximization"
  Abstract: Ensemble learning by variational free energy minimization is a tool introduced to neural networks by Hinton and van Camp in which learning is described in terms of the optimization of an ensemble of parameter vectors. The optimized ensemble is an approximation to the posterior probability distribution of the parameters. This tool has now been applied to a variety of statistical inference problems. In this paper I study a linear regression model with both parameters and hyper-parameters. I demonstrate that the evidence approximation for the optimization of regularization constants can be derived in detail from a free energy minimization viewpoint.

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:
    - "Developments in Probabilistic Modelling with Neural Networks|Ensemble Learning"

Connected through hyperedge HE-2 (induced by source paper "A Practical Bayesian Framework for Backprop Networks"):
  with members:
    - "Density Networks and their Application to Protein Modelling"
    - "PREDICTING SUNSPOTS AND EXCHANGE RATES WITH CONNECTIONIST NETWORKS"
    - "FLAT MINIMA Neural Computation 9(1):1-42 (1997)"
    - "Using Neural Networks for Descriptive Statistical Analysis of Educational Data"
    - "Probabilistic Networks: New Models and New Methods"
    - "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights"
    - "Bayesian Non-linear Modelling for the Prediction Competition"
    - "MDL and MML Similarities and Differences (Introduction to Minimum Encoding Inference Part III)"

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 for Hidden Markov Models"
    - "Developments in Probabilistic Modelling with Neural Networks|Ensemble Learning"
    - "Probabilistic Networks: New Models and New Methods"
    - "A Practical Bayesian Framework for Backprop Networks"

Connected through hyperedge HE-4 (induced by source paper "Developments in Probabilistic Modelling with Neural Networks|Ensemble Learning"):
  with members:
    - "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"
    - "Ensemble Learning for Hidden Markov Models"
    - "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights"
    - "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