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: "Networks of Spiking Neurons: The Third Generation of Neural Network Models"
  Abstract: The computational power of formal models for networks of spiking neurons is compared with that of other neural network models based on McCulloch Pitts neurons (i.e. threshold gates) respectively sigmoidal gates. In particular it is shown that networks of spiking neurons are computationally more powerful than these other neural network models. A concrete biologically relevant function is exhibited which can be computed by a single spiking neuron (for biologically reasonable values of its parameters), but which requires hundreds of hidden units on a sigmoidal neural net. This article does not assume prior knowledge about spiking neurons, and it contains an extensive list of references to the currently available literature on computations in networks of spiking neurons and relevant results from neuro biology.

Connected through hyperedge HE-1 (induced by source paper "An Efficient Implementation of Sigmoidal Neural Nets in Temporal Coding with Noisy Spiking Neurons"):
  with members:
    - "Associative memory using action potential timing"
    - "A Delay-Line Based Motion Detection Chip"

Connected through hyperedge HE-2 (induced by source paper "Shattering all sets of k points in "general position" requires (k 1)=2 parameters"):
  with members:
    - "Neural Networks with Quadratic VC Dimension"
    - "Critical Points for Least-Squares Problems Involving Certain Analytic Functions, with Applications to Sigmoidal Nets"

Connected through hyperedge HE-3 (induced by source paper "A Delay-Line Based Motion Detection Chip"):
  with members:
    - "An Efficient Implementation of Sigmoidal Neural Nets in Temporal Coding with Noisy Spiking Neurons"
    - "VISIT: An Efficient Computational Model of Human Visual Attention"

Connected through hyperedge HE-4 (induced by source paper "Vapnik-Chervonenkis Dimension of Recurrent Neural Networks"):
  with members:
    - "Neural Networks with Quadratic VC Dimension"
    - "Sample Complexity for Learning Recurrent Perceptron Mappings"
    - "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"

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