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: "Interconnected Automata and Linear Systems: A Theoretical Framework in Discrete-Time In Hybrid Systems III: Verification"
  Abstract: This paper summarizes the definitions and several of the main results of an approach to hybrid systems, which combines finite automata and linear systems, developed by the author in the early 1980s. Some related more recent results are briefly mentioned as well.

Connected through hyperedge HE-1 (induced by source paper "POWER OF NEURAL NETS"):
  with members:
    - "Vapnik-Chervonenkis Dimension of Recurrent Neural Networks"
    - "Dimension of Recurrent Neural Networks"
    - "Facing The Facts: Necessary Requirements For The Artificial Evolution of Complex Behaviour"
    - "Fault-Tolerant Implementation of Finite-State Automata in Recurrent Neural Networks"
    - "Noisy Time Series Prediction using Symbolic Representation and Recurrent Neural Network Grammatical Inference"
    - "Can Recurrent Neural Networks Learn Natural Language Grammars? W&Z recurrent neural networks are able to"
    - "First-Order vs. Second-Order Single Layer 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"
    - "Using Fourier-Neural Recurrent Networks to Fit Sequential Input/Output Data"

Connected through hyperedge HE-3 (induced by source paper "LINEAR SYSTEMS WITH SIGN-OBSERVATIONS"):
  with members:
    - "Sample Complexity for Learning Recurrent Perceptron Mappings"
    - "BACKPROPAGATION SEPARATES WHERE PERCEPTRONS DO"
    - "Lemma 2.3 The system is reachable and observable and realizes the same input/output behavior as"
    - "Observability of Linear Systems with Saturated Outputs"

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