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: "Recurrent Neural Networks: Some Systems-Theoretic Aspects"
  Abstract: This paper provides an exposition of some recent research regarding system-theoretic aspects of continuous-time recurrent (dynamic) neural networks with sigmoidal activation functions. The class of systems is introduced and discussed, and a result is cited regarding their universal approximation properties. Known characterizations of controllability, ob-servability, and parameter identifiability are reviewed, as well as a result on minimality. Facts regarding the computational power of recurrent nets are also mentioned. fl Supported in part by US Air Force Grant AFOSR-94-0293

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"
    - "OBSERVABILITY IN RECURRENT NEURAL NETWORKS"
    - "Using Fourier-Neural Recurrent Networks to Fit Sequential Input/Output Data"

Connected through hyperedge HE-2 (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"
    - "NETWORKS, FUNCTION DETERMINES FORM"
    - "Identification and Control of Nonlinear Systems Using Neural Network Models: Design and Stability Analysis"
    - "FEEDBACK STABILIZATION USING TWO-HIDDEN-LAYER NETS"

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"
    - "NETWORKS, FUNCTION DETERMINES FORM"
    - "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 "Complete Controllability of Continuous-Time Recurrent Neural Networks"):
  with members:
    (none)

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