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: "LINEAR SYSTEMS WITH SIGN-OBSERVATIONS"
  Abstract: This paper deals with systems that are obtained from linear time-invariant continuous-or discrete-time devices followed by a function that just provides the sign of each output. Such systems appear naturally in the study of quantized observations as well as in signal processing and neural network theory. Results are given on observability, minimal realizations, and other system-theoretic concepts. Certain major differences exist with the linear case, and other results generalize in a surprisingly straightforward manner.

Connected through hyperedge HE-1 (induced by source paper "Sample Complexity for Learning Recurrent Perceptron Mappings"):
  with members:
    - "Vapnik-Chervonenkis Dimension of Recurrent Neural Networks"
    - "Dimension of Recurrent Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "BACKPROPAGATION SEPARATES WHERE PERCEPTRONS DO"):
  with members:
    - "BACKPROPAGATION CAN GIVE RISE TO SPURIOUS LOCAL MINIMA EVEN FOR NETWORKS WITHOUT HIDDEN LAYERS"
    - "Exponentially many local minima for single neurons"

Connected through hyperedge HE-3 (induced by source paper "Lemma 2.3 The system is reachable and observable and realizes the same input/output behavior as"):
  with members:
    (none)

Connected through hyperedge HE-4 (induced by source paper "Interconnected Automata and Linear Systems: A Theoretical Framework in Discrete-Time In Hybrid Systems III: Verification"):
  with members:
    - "POWER OF NEURAL NETS"
    - "OBSERVABILITY IN 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