human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Sample Complexity for Learning Recurrent Perceptron Mappings":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Vapnik-Chervonenkis Dimension of Recurrent Neural Networks")
  Member "Interconnected Automata and Linear Systems: A Theoretical Framework in Discrete-Time In Hybrid Systems III: Verification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "POWER OF NEURAL NETS")
  Member "BACKPROPAGATION SEPARATES WHERE PERCEPTRONS DO":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Exponentially many local minima for single neurons")
  Member "Lemma 2.3 The system is reachable and observable and realizes the same input/output behavior as":
    also connected through other hyperedges:
      (none)
  Member "Observability of Linear Systems with Saturated Outputs":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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