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 "Sample Complexity for Learning Recurrent Perceptron Mappings"):
  Abstract: Recurrent perceptron classifiers generalize the classical perceptron model. They take into account those correlations and dependences among input coordinates which arise from linear digital filtering. This paper provides tight bounds on sample complexity associated to the fitting of such models to experimental data.

Includes members:
  Member "Vapnik-Chervonenkis Dimension of Recurrent Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "POWER OF NEURAL NETS")
  Member "Dimension of Recurrent Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "POWER OF NEURAL NETS")
  Member "LINEAR SYSTEMS WITH SIGN-OBSERVATIONS":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "BACKPROPAGATION SEPARATES WHERE PERCEPTRONS DO")

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