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: "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights"
  Abstract: Supervised neural networks generalize well if there is much less information in the weights than there is in the output vectors of the training cases. So during learning, it is important to keep the weights simple by penalizing the amount of information they contain. The amount of information in a weight can be controlled by adding Gaussian noise and the noise level can be adapted during learning to optimize the trade-off between the expected squared error of the network and the amount of information in the weights. We describe a method of computing the derivatives of the expected squared error and of the amount of information in the noisy weights in a network that contains a layer of non-linear hidden units. Provided the output units are linear, the exact derivatives can be computed efficiently without time-consuming Monte Carlo simulations. The idea of minimizing the amount of information that is required to communicate the weights of a neural network leads to a number of interesting schemes for encoding the weights.

Connected through hyperedge HE-1 (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"
    - "Ensemble Learning and Evidence Maximization"
    - "Probabilistic Networks: New Models and New Methods"
    - "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-2 (induced by source paper "Probabilistic Networks: New Models and New Methods"):
  with members:
    - "A Practical Bayesian Framework for Backprop Networks"
    - "Regression with Input-dependent Noise: A Gaussian Process Treatment"
    - "Interpolation Models with Multiple"
    - "Bayesian Non-linear Modelling for the Prediction Competition"

Connected through hyperedge HE-3 (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"
    - "Developments in Probabilistic Modelling with Neural Networks|Ensemble Learning"
    - "Choice of Basis for Laplace Approximation"

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"
    - "Developments in Probabilistic Modelling with Neural Networks|Ensemble Learning"
    - "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 "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 and Evidence Maximization"
    - "Ensemble Learning for Hidden Markov Models"
    - "Free Energy Minimization Algorithm for Decoding and Cryptanalysis three binary vectors: s of length N"

Connected through hyperedge HE-6 (induced by source paper "FLAT MINIMA Neural Computation 9(1):1-42 (1997)"):
  with members:
    - "DISCOVERING NEURAL NETS WITH LOW KOLMOGOROV COMPLEXITY AND HIGH GENERALIZATION CAPABILITY Neural Networks 10(5):857-873, 1997"
    - "ON LEARNING HOW TO LEARN LEARNING STRATEGIES"
    - "GUESSING CAN OUTPERFORM MANY LONG TIME LAG ALGORITHMS"
    - "A Practical Bayesian Framework for Backprop 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: Neural_Networks