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: "Using Fourier-Neural Recurrent Networks to Fit Sequential Input/Output Data"
  Abstract: This paper suggests the use of Fourier-type activation functions in fully recurrent neural networks. The main theoretical advantage is that, in principle, the problem of recovering internal coefficients from input/output data is solvable in closed form.

Connected through hyperedge HE-1 (induced by source paper "NETWORKS, FUNCTION DETERMINES FORM"):
  with members:
    - "NEURAL NETS AS SYSTEMS MODELS AND CONTROLLERS suitability of "neural nets" as models for dynamical"
    - "Recurrent Neural Networks: Some Systems-Theoretic Aspects"
    - "OBSERVABILITY IN RECURRENT NEURAL NETWORKS"

Connected through hyperedge HE-2 (induced by source paper "OBSERVABILITY IN RECURRENT NEURAL NETWORKS"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "NETWORKS, FUNCTION DETERMINES FORM"
    - "Recurrent Neural Networks: Some Systems-Theoretic Aspects"
    - "Interconnected Automata and Linear Systems: A Theoretical Framework in Discrete-Time In Hybrid Systems III: Verification"

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