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: "Brief Papers Computing Second Derivatives in Feed-Forward Networks: A Review"
  Abstract: The calculation of second derivatives is required by recent training and analysis techniques of connectionist networks, such as the elimination of superfluous weights, and the estimation of confidence intervals both for weights and network outputs. We here review and develop exact and approximate algorithms for calculating second derivatives. For networks with jwj weights, simply writing the full matrix of second derivatives requires O(jwj 2 ) operations. For networks of radial basis units or sigmoid units, exact calculation of the necessary intermediate terms requires of the order of 2h + 2 backward/forward-propagation passes where h is the number of hidden units in the network. We also review and compare three approximations (ignoring some components of the second derivative, numerical differentiation, and scoring). Our algorithms apply to arbitrary activation functions, networks, and error functions (for instance, with connections that skip layers, or radial basis functions, or cross-entropy error and Softmax units, etc.).

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"
    - "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights"
    - "Bayesian Non-linear Modelling for the Prediction Competition"

Connected through hyperedge HE-2 (induced by source paper "A comparison of some error estimates for neural network models  Summary"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "A Practical Bayesian Framework for Backprop Networks"
    - "Penalisation multiple adaptative un nouvel algorithme de regression, la penalisation multiple adapta-tive. Cet algorithme represente"
    - "Evaluating Neural Network Predictors by Bootstrapping"
    - "Predictions with Confidence Intervals (Local Error Bars)"

Connected through hyperedge HE-3 (induced by source paper "The Free Speech  Phoneme Probability Estimation with Dynamic Sparsely Connected Artificial Neural Networks"):
  with members:
    - "Using the Grow-And-Prune Network to Solve Problems of Large Dimensionality"

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