human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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

Includes members:
  Member "Learning networks for face analysis and synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Receptive Fields for Vision: from Hyperacuity to Object Recognition")
  Member "Cortical Mechanisms of Visual Recognition and Learning: A Hierarchical Kalman Filter Model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "The wake-sleep algorithm for unsupervised neural networks")
  Member "Prior Information and Generalized Questions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "PRIOR KNOWLEDGE AND THE CREATION OF "VIRTUAL" EXAMPLES FOR RBF NETWORKS 1")
  Member "PRIOR KNOWLEDGE AND THE CREATION OF "VIRTUAL" EXAMPLES FOR RBF NETWORKS 1":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Learning networks for face analysis and synthesis")
  Member "In Advances in Neural Information Processing Systems 8  Gaussian Processes for Regression":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Learning networks for face analysis and synthesis")
  Member "for Projective Basis Function Networks 2m1 Global Form 2m Local Form With appropriate constant factors,":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Learning networks for face analysis and synthesis")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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