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: "A Practical Bayesian Framework for Backprop Networks"
  Abstract: A quantitative and practical Bayesian framework is described for learning of mappings in feedforward networks. The framework makes possible: (1) objective comparisons between solutions using alternative network architectures; (2) objective stopping rules for network pruning or growing procedures; (3) objective choice of magnitude and type of weight decay terms or additive regularisers (for penalising large weights, etc.); (4) a measure of the effective number of well-determined parameters in a model; (5) quantified estimates of the error bars on network parameters and on network output; (6) objective comparisons with alternative learning and interpolation models such as splines and radial basis functions. The Bayesian `evidence' automatically embodies `Occam's razor,' penalising over-flexible and over-complex models. The Bayesian approach helps detect poor underlying assumptions in learning models. For learning models well matched to a problem, a good correlation between generalisation ability and the Bayesian evidence is obtained. This paper makes use of the Bayesian framework for regularisation and model comparison described in the companion paper `Bayesian interpolation' (MacKay, 1991a). This framework is due to Gull and Skilling (Gull, 1989a).

Connected through hyperedge HE-1 (induced by source paper "Information-based objective functions for active data selection"):
  with members:
    - "In:  A Mixture Model System for Medical and Machine Diagnosis"
    - "In  Active Learning with Statistical Models"
    - "Heterogeneous Uncertainty Sampling for Supervised Learning"
    - "Improving Generalization with Active Learning"
    - "REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS"
    - "Advances in Neural Information Processing Systems 8 Active Learning in Multilayer Perceptrons"
    - "An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks."

Connected through hyperedge HE-2 (induced by source paper "Brief Papers Computing Second Derivatives in Feed-Forward Networks: A Review"):
  with members:
    - "A comparison of some error estimates for neural network models  Summary"
    - "The Free Speech  Phoneme Probability Estimation with Dynamic Sparsely Connected Artificial Neural Networks"

Connected through hyperedge HE-3 (induced by source paper "In Advances in Neural Information Processing Systems 8  Gaussian Processes for Regression"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "Regularization Theory and Neural Networks Architectures"
    - "A Practical Monte Carlo Implementation of Bayesian Learning"

Connected through hyperedge HE-4 (induced by source paper "Selecting Input Variables Using Mutual Information and Nonparametric Density Estimation"):
  with members:
    - "Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation"
    - "The Observer-Observation Dilemma in Neuro-Forecasting: Reliable Models From Unreliable Data Through CLEARNING"

Connected through hyperedge HE-5 (induced by source paper "Density Networks and their Application to Protein Modelling"):
  with members:
    - "Hidden Markov Models in Computational Biology: Applications to Protein Modeling UCSC-CRL-93-32 Keywords: Hidden Markov Models,"

Connected through hyperedge HE-6 (induced by source paper "Bayesian Training of Backpropagation Networks by the Hybrid Monte Carlo Method"):
  with members:
    - "Priors for Infinite Networks"
    - "5 Bayesian estimation 5.1 Introduction"

Connected through hyperedge HE-7 (induced by source paper "Bayesian Learning in Feed Forward Neural Networks"):
  with members:
    - "ADAPTIVE REGULARIZATION"

Connected through hyperedge HE-8 (induced by source paper "Probabilistic Networks: New Models and New Methods"):
  with members:
    - "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights"
    - "Regression with Input-dependent Noise: A Gaussian Process Treatment"
    - "Interpolation Models with Multiple"
    - "Bayesian Non-linear Modelling for the Prediction Competition"

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