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: "PRIOR KNOWLEDGE AND THE CREATION OF "VIRTUAL" EXAMPLES FOR RBF NETWORKS 1"
  Abstract: We consider the problem of how to incorporate prior knowledge in supervised learning techniques. We set the problem in the framework of regularization theory, and consider the case in which we know that the approximated function has radial symmetry. The problem can be solved in two alternative ways: 1) use the invariance as a constraint in the regularization theory framework to derive a rotation invariant version of Radial Basis Functions; 2) use the radial symmetry to create new, "virtual" examples from a given data set. We show that these two apparently different methods of learning from "hints" (Abu-Mostafa, 1993) lead to exactly the same analyt ical solution.

Connected through hyperedge HE-1 (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"
    - "Regularization Theory and Neural Networks Architectures"
    - "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-2 (induced by source paper "Regularization Theory and Neural Networks Architectures"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "Cortical Mechanisms of Visual Recognition and Learning: A Hierarchical Kalman Filter Model"
    - "Prior Information and Generalized Questions"
    - "In Advances in Neural Information Processing Systems 8  Gaussian Processes for Regression"
    - "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 Information and Generalized Questions"):
  with members:
    - "Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo"
    - "Regularization Theory and Neural Networks Architectures"

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