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: "NEURAL NETS AS SYSTEMS MODELS AND CONTROLLERS suitability of "neural nets" as models for dynamical"
  Abstract: This paper briefly surveys some recent results relevant

Connected through hyperedge HE-1 (induced by source paper "FEEDBACK STABILIZATION USING TWO-HIDDEN-LAYER NETS"):
  with members:
    - "Identification and Control of Nonlinear Systems Using Neural Network Models: Design and Stability Analysis"
    - "FEEDBACK STABILIZATION OF NONLINEAR SYSTEMS"

Connected through hyperedge HE-2 (induced by source paper "NETWORKS, FUNCTION DETERMINES FORM"):
  with members:
    - "Recurrent Neural Networks: Some Systems-Theoretic Aspects"
    - "OBSERVABILITY IN RECURRENT NEURAL NETWORKS"
    - "Using Fourier-Neural Recurrent Networks to Fit Sequential Input/Output Data"

Connected through hyperedge HE-3 (induced by source paper "Identification and Control of Nonlinear Systems Using Neural Network Models: Design and Stability Analysis"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Learning networks for face analysis and synthesis"
    - "FEEDBACK STABILIZATION USING TWO-HIDDEN-LAYER NETS"
    - "Space-Frequency Localized Basis Function Networks for Nonlinear System Estimation and Control"

Connected through hyperedge HE-4 (induced by source paper "Recurrent Neural Networks: Some Systems-Theoretic Aspects"):
  with members:
    - "NETWORKS, FUNCTION DETERMINES FORM"
    - "OBSERVABILITY IN RECURRENT NEURAL NETWORKS"
    - "Complete Controllability of Continuous-Time Recurrent Neural Networks"

Connected through hyperedge HE-5 (induced by source paper "Vapnik-Chervonenkis Dimension of Recurrent Neural Networks"):
  with members:
    - "Neural Networks with Quadratic VC Dimension"
    - "Sample Complexity for Learning Recurrent Perceptron Mappings"
    - "On the Effect of Analog Noise in Discrete-Time Analog Computations"
    - "POWER OF NEURAL NETS"
    - "What Size Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation"
    - "Networks of Spiking Neurons: The Third Generation of Neural Network Models"

Connected through hyperedge HE-6 (induced by source paper "Dimension of Recurrent Neural Networks"):
  with members:
    - "Neural Networks with Quadratic VC Dimension"
    - "Sample Complexity for Learning Recurrent Perceptron Mappings"
    - "POWER OF NEURAL NETS"

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