human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "s4nn temporal backpropagation for spiking neural networks with one spike per neuron"
  Abstract: We propose a new supervised learning rule for multilayer spiking neural networks (SNN) that use a form of temporal coding known as rank-order-coding. With this coding scheme, all neurons fire exactly one spike per stimulus, but the firing order carries information. In particular, in the readout layer, the first neuron to fire determines the class of the stimulus. We derive a new learning rule for this sort of network, termed S4NN, akin to traditional error backpropagation, yet based on latencies. We show how approximate error gradients can be computed backward in a feedforward network with an arbitrary number of layers. This approach reaches state-of-the-art performance with SNNs: test accuracy of 97.4% for the MNIST dataset, and of 99.2% for the Caltech Face/Motorbike dataset. Yet the neuron model we use, non-leaky integrate-and-fire, are simpler and more hardware friendly than the one used in all previous similar proposals.

Connected through hyperedge HE-1 (induced by source paper "a deep unsupervised feature learning spiking neural network with binarized classification layers for emnist classification"):
  with members:
    - "a spiking network for inference of relations trained with neuromorphic backpropagation"
    - "deep convolutional spiking neural networks for image classification"
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "towards scalable efficient and accurate deep spiking neural networks with backward residual connections stochastic softmax and hybridization"
    - "spyketorch efficient simulation of convolutional spiking neural networks with at most one spike per neuron"
    - "dendritic inspired processing enables bio plausible stdp in compound binary synapses"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "stdp based spiking deep convolutional neural networks for object recognition"

Connected through hyperedge HE-2 (induced by source paper "recognizing images with at most one spike per neuron"):
  with members:
    - "randaugment practical data augmentation with no separate search"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "squeeze and excitation networks"
    - "searching for activation functions"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "classifying images with few spikes per neuron"):
  with members:
    - "recognizing images with at most one spike per neuron"
    - "enabling spike based backpropagation in state of the art deep neural network architectures"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-4 (induced by source paper "fast and deep neuromorphic learning with time to first spike coding"):
  with members:
    - "biologically plausible deep learning but how far can we go with shallow networks"
    - "deep spiking neural network with spike count based learning rule"
    - "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"
    - "deep learning in spiking neural networks"
    - "spinnaker 2 a 10 million core processor system for brain simulation and machine learning"
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "surrogate gradient learning in spiking neural networks"
    - "demonstrating advantages of neuromorphic computation a pilot study"

Connected through hyperedge HE-5 (induced by source paper "technical report supervised training of convolutional spiking neural networks with pytorch"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "surrogate gradient learning in spiking neural networks"
    - "bio inspired digit recognition using reward modulated spike timing dependent plasticity in deep convolutional networks"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "long short term memory and learning to learn in networks of spiking neurons"

Connected through hyperedge HE-6 (induced by source paper "action recognition using supervised spiking neural networks"):
  with members:
    - "deep convolutional spiking neural networks for image classification"
    - "deep learning in spiking neural networks"
    - "surrogate gradient learning in spiking neural networks"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "long short term memory and learning to learn in networks of spiking neurons"

Question: Please tell me which class the center node 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)