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: "NETWORKS, FUNCTION DETERMINES FORM"
  Abstract: Report SYCON-92-03 ABSTRACT This paper shows that the weights of continuous-time feedback neural networks are uniquely identifiable from input/output measurements. Under very weak genericity assumptions, the following is true: Assume given two nets, whose neurons all have the same nonlinear activation function ; if the two nets have equal behaviors as "black boxes" then necessarily they must have the same number of neurons and |except at most for sign reversals at each node| the same weights.

Connected through hyperedge HE-1 (induced by source paper "NEURAL NETS AS SYSTEMS MODELS AND CONTROLLERS suitability of "neural nets" as models for dynamical"):
  with members:
    - "Vapnik-Chervonenkis Dimension of Recurrent Neural Networks"
    - "Dimension of Recurrent Neural Networks"
    - "Identification and Control of Nonlinear Systems Using Neural Network Models: Design and Stability Analysis"
    - "FEEDBACK STABILIZATION USING TWO-HIDDEN-LAYER NETS"
    - "Recurrent Neural Networks: Some Systems-Theoretic Aspects"

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

Connected through hyperedge HE-3 (induced by source paper "OBSERVABILITY IN RECURRENT NEURAL NETWORKS"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Recurrent Neural Networks: Some Systems-Theoretic Aspects"
    - "Interconnected Automata and Linear Systems: A Theoretical Framework in Discrete-Time In Hybrid Systems III: Verification"
    - "Using Fourier-Neural Recurrent Networks to Fit Sequential Input/Output Data"

Connected through hyperedge HE-4 (induced by source paper "Recurrent Neural Networks: Some Systems-Theoretic Aspects"):
  with members:
    - "NEURAL NETS AS SYSTEMS MODELS AND CONTROLLERS suitability of "neural nets" as models for dynamical"
    - "OBSERVABILITY IN RECURRENT NEURAL NETWORKS"
    - "Complete Controllability of Continuous-Time Recurrent Neural Networks"

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