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 "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.

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 "Shattering all sets of k points in "general position" requires (k 1)=2 parameters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Neural Networks with Quadratic VC Dimension")
  Member "An Efficient Implementation of Sigmoidal Neural Nets in Temporal Coding with Noisy Spiking Neurons":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Associative memory using action potential timing")
  Member "A Delay-Line Based Motion Detection Chip":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "An Efficient Implementation of Sigmoidal Neural Nets in Temporal Coding with Noisy Spiking Neurons")

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