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: "Regularization Theory and Neural Networks Architectures"
  Abstract: We had previously shown that regularization principles lead to approximation schemes which are equivalent to networks with one layer of hidden units, called Regularization Networks. In particular, standard smoothness functionals lead to a subclass of regularization networks, the well known Radial Basis Functions approximation schemes. This paper shows that regularization networks encompass a much broader range of approximation schemes, including many of the popular general additive models and some of the neural networks. In particular, we introduce new classes of smoothness functionals that lead to different classes of basis functions. Additive splines as well as some tensor product splines can be obtained from appropriate classes of smoothness functionals. Furthermore, the same generalization that extends Radial Basis Functions (RBF) to Hyper Basis Functions (HBF) also leads from additive models to ridge approximation models, containing as special cases Breiman's hinge functions, some forms of Projection Pursuit Regression and several types of neural networks. We propose to use the term Generalized Regularization Networks for this broad class of approximation schemes that follow from an extension of regularization. In the probabilistic interpretation of regularization, the different classes of basis functions correspond to different classes of prior probabilities on the approximating function spaces, and therefore to different types of smoothness assumptions. In summary, different multilayer networks with one hidden layer, which we collectively call Generalized Regularization Networks, correspond to different classes of priors and associated smoothness functionals in a classical regularization principle. Three broad classes are a) Radial Basis Functions that can be generalized to Hyper Basis Functions, b) some tensor product splines, and c) additive splines that can be generalized to schemes of the type of ridge approximation, hinge functions and several perceptron-like neural networks with one-hidden layer. 1 This paper will appear on Neural Computation, vol. 7, pages 219-269, 1995. An earlier version of

Connected through hyperedge HE-1 (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"
    - "A Practical Bayesian Framework for Backprop Networks"
    - "A Practical Monte Carlo Implementation of Bayesian Learning"

Connected through hyperedge HE-2 (induced by source paper "Learning networks for face analysis and synthesis"):
  with members:
    - "Active Gesture Recognition using Partially Observable Markov Decision Processes"
    - "A Delay Damage Model Selection Algorithm for NARX Neural Networks"
    - "Equivalence of Linear Boltzmann Chains and Hidden Markov Models sequence L, is: where Z(; A;"
    - "Learning from incomplete data"
    - "PRIOR KNOWLEDGE AND THE CREATION OF "VIRTUAL" EXAMPLES FOR RBF NETWORKS 1"
    - "In Advances in Neural Information Processing Systems 8  Gaussian Processes for Regression"
    - "Efficient Implementation of Gaussian Processes"
    - "for Projective Basis Function Networks 2m1 Global Form 2m Local Form With appropriate constant factors,"

Connected through hyperedge HE-3 (induced by source paper "PRIOR KNOWLEDGE AND THE CREATION OF "VIRTUAL" EXAMPLES FOR RBF NETWORKS 1"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "Prior Information and Generalized Questions"

Connected through hyperedge HE-4 (induced by source paper "Cortical Mechanisms of Visual Recognition and Learning: A Hierarchical Kalman Filter Model"):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "The wake-sleep algorithm for unsupervised neural networks"

Connected through hyperedge HE-5 (induced by source paper "for Projective Basis Function Networks 2m1 Global Form 2m Local Form With appropriate constant factors,"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "From Data Distributions to Regularization in Invariant Learning"

Connected through hyperedge HE-6 (induced by source paper "Prior Information and Generalized Questions"):
  with members:
    - "Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo"
    - "PRIOR KNOWLEDGE AND THE CREATION OF "VIRTUAL" EXAMPLES FOR RBF NETWORKS 1"

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