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: "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.

Connected through hyperedge HE-1 (induced by source paper "LINEAR SYSTEMS WITH SIGN-OBSERVATIONS"):
  with members:
    - "Interconnected Automata and Linear Systems: A Theoretical Framework in Discrete-Time In Hybrid Systems III: Verification"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "Vapnik-Chervonenkis Dimension of Recurrent Neural Networks"):
  with members:
    - "Neural Networks with Quadratic VC Dimension"
    - "NEURAL NETS AS SYSTEMS MODELS AND CONTROLLERS suitability of "neural nets" as models for dynamical"
    - "On the Effect of Analog Noise in Discrete-Time Analog Computations"
    - "POWER OF NEURAL NETS"
    - "What Size Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation"
    - "Networks of Spiking Neurons: The Third Generation of Neural Network Models"

Connected through hyperedge HE-3 (induced by source paper "Dimension of Recurrent Neural Networks"):
  with members:
    - "Neural Networks with Quadratic VC Dimension"
    - "NEURAL NETS AS SYSTEMS MODELS AND CONTROLLERS suitability of "neural nets" as models for dynamical"
    - "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