human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a deep unsupervised feature learning spiking neural network with binarized classification layers for emnist classification"):
  Abstract: End user AI is trained on large server farms with data collected from the users. With ever increasing demand for IOT devices, there is a need for deep learning approaches that can be implemented (at the edge) in an energy efficient manner. In this work we approach this using spiking neural networks. The unsupervised learning technique of spike timing dependent plasticity (STDP) using binary activations are used to extract features from spiking input data. Gradient descent (backpropagation) is used only on the output layer to perform the training for classification. The accuracies obtained for the balanced EMNIST data set compare favorably with other approaches. The effect of stochastic gradient descent (SGD) approximations on learning capabilities of our network are also explored.

Includes members:
  Member "a spiking network for inference of relations trained with neuromorphic backpropagation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spikegrad an ann equivalent computation model for implementing backpropagation with spikes")
  Member "deep convolutional spiking neural networks for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "s4nn temporal backpropagation for spiking neural networks with one spike per neuron")
  Member "temporal coding in spiking neural networks with alpha synaptic function":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a supervised learning algorithm for multilayer spiking neural networks based on temporal coding toward energy efficient vlsi processor design")
  Member "s4nn temporal backpropagation for spiking neural networks with one spike per neuron":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "recognizing images with at most one spike per neuron")
  Member "towards scalable efficient and accurate deep spiking neural networks with backward residual connections stochastic softmax and hybridization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spike flownet event based optical flow estimation with energy efficient hybrid neural networks")
  Member "spyketorch efficient simulation of convolutional spiking neural networks with at most one spike per neuron":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "design space exploration of hardware spiking neurons for embedded artificial intelligence")
  Member "dendritic inspired processing enables bio plausible stdp in compound binary synapses":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep convolutional spiking neural networks for image classification")
  Member "hybrid macro micro level backpropagation for training deep spiking neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rmp snns residual membrane potential neuron for enabling deeper high accuracy and low latency spiking neural networks")

Question: Please tell me which class the source paper belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.NE(Neural and Evolutionary Computing)