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: "direct training for spiking neural networks faster larger better"
  Abstract: Spiking neural networks (SNNs) that enables energy efficient implementation on emerging neuromorphic hardware are gaining more attention. Yet now, SNNs have not shown competitive performance compared with artificial neural networks (ANNs), due to the lack of effective learning algorithms and efficient programming frameworks. We address this issue from two aspects: (1) We propose a neuron normalization technique to adjust the neural selectivity and develop a direct learning algorithm for deep SNNs. (2) Via narrowing the rate coding window and converting the leaky integrate-and-fire (LIF) model into an explicitly iterative version, we present a Pytorch-based implementation method towards the training of large-scale SNNs. In this way, we are able to train deep SNNs with tens of times speedup. As a result, we achieve significantly better accuracy than the reported works on neuromorphic datasets (N-MNIST and DVS-CIFAR10), and comparable accuracy as existing ANNs and pre-trained SNNs on non-spiking datasets (CIFAR10). {To our best knowledge, this is the first work that demonstrates direct training of deep SNNs with high performance on CIFAR10, and the efficient implementation provides a new way to explore the potential of SNNs.

Connected through hyperedge HE-1 (induced by source paper "exploring adversarial attack in spiking neural networks with spike compatible gradient"):
  with members:
    - "a comprehensive analysis on adversarial robustness of spiking neural networks"
    - "snn under attack are spiking deep belief networks vulnerable to adversarial examples"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "adversarial patch"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-2 (induced by source paper "temporal spike sequence learning via backpropagation for deep spiking neural networks"):
  with members:
    - "deep learning in spiking neural networks"
    - "spike train level backpropagation for training deep recurrent spiking neural networks"
    - "pytorch an imperative style high performance deep learning library"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "slayer spike layer error reassignment in time"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "training deep spiking neural networks using backpropagation"

Connected through hyperedge HE-3 (induced by source paper "exploiting neuron and synapse filter dynamics in spatial temporal learning of deep spiking neural network"):
  with members:
    - "surrogate gradient learning in spiking neural networks"
    - "embodied event driven random backpropagation"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "synaptic plasticity dynamics for deep continuous local learning decolle"
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "slayer spike layer error reassignment in time"
    - "bp stdp approximating backpropagation using spike timing dependent plasticity"

Connected through hyperedge HE-4 (induced by source paper "comprehensive snn compression using admm optimization and activity regularization"):
  with members:
    - "restocnet residual stochastic binary convolutional spiking neural network for memory efficient neuromorphic computing"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "training and inference with integers in deep neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "progressive weight pruning of deep neural networks using admm"
    - "spiking deep residual network"

Connected through hyperedge HE-5 (induced by source paper "s4nn temporal backpropagation for spiking neural networks with one spike per neuron"):
  with members:
    - "recognizing images with at most one spike per neuron"
    - "biologically plausible deep learning but how far can we go with shallow networks"
    - "spikegrad an ann equivalent computation model for implementing backpropagation with spikes"
    - "deep convolutional spiking neural networks for image classification"
    - "deep learning in spiking neural networks"
    - "fast and deep neuromorphic learning with time to first spike coding"
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "surrogate gradient learning in spiking neural networks"

Connected through hyperedge HE-6 (induced by source paper "a tandem learning rule for efficient and rapid inference on deep spiking neural networks"):
  with members:
    - "deep spiking neural network with spike count based learning rule"
    - "deep learning in spiking neural networks"
    - "surrogate gradient learning in spiking neural networks"
    - "enabling spike based backpropagation in state of the art deep neural network architectures"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "spiking deep residual network"
    - "slayer spike layer error reassignment in time"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-7 (induced by source paper "enabling spike based backpropagation in state of the art deep neural network architectures"):
  with members:
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "slayer spike layer error reassignment in time"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "unsupervised regenerative learning of hierarchical features in spiking deep networks for object recognition"

Connected through hyperedge HE-8 (induced by source paper "biologically plausible deep learning but how far can we go with shallow networks"):
  with members:
    - "unsupervised learning by competing hidden units"
    - "deep learning in spiking neural networks"
    - "putting a bug in ml the moth olfactory network learns to read mnist"
    - "error forward propagation reusing feedforward connections to propagate errors in deep learning"
    - "feedback alignment in deep convolutional networks"
    - "regularized evolution for image classifier architecture search"
    - "a biologically plausible learning rule for deep learning in the brain"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"

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)