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: "OBSERVABILITY IN RECURRENT NEURAL NETWORKS"
  Abstract: Report SYCON-92-07rev ABSTRACT We obtain a characterization of observability for a class of nonlinear systems which appear in neural networks research.

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"
    - "Using Fourier-Neural Recurrent Networks to Fit Sequential Input/Output Data"

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

Connected through hyperedge HE-3 (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"
    - "NETWORKS, FUNCTION DETERMINES FORM"
    - "Complete Controllability of Continuous-Time Recurrent Neural Networks"

Connected through hyperedge HE-4 (induced by source paper "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"):
  with members:
    - "Representing and Learning Visual Schemas in Neural Networks for Scene Analysis"
    - "Convergence-Zone Episodic Memory: Analysis and Simulations"
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Dyslexic and Category-Specific Aphasic Impairments in a Self-Organizing Feature Map Model of the Lexicon"
    - "Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference,"
    - "Boltzmann Machine learning using mean field theory and linear response correction"
    - "Induction of Multiscale Temporal Structure"

Connected through hyperedge HE-5 (induced by source paper "Interconnected Automata and Linear Systems: A Theoretical Framework in Discrete-Time In Hybrid Systems III: Verification"):
  with members:
    - "LINEAR SYSTEMS WITH SIGN-OBSERVATIONS"
    - "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