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: "spiking deep residual network"
  Abstract: Recently, spiking neural network (SNN) has received significant attentions for its biological plausibility. SNN theoretically has at least the same computational power as traditional artificial neural networks (ANNs), and it has the potential to achieve revolutionary energy-efficiency. However, at current stage, it is still a big challenge to train a very deep SNN. In this paper, we propose an efficient approach to build a spiking version of deep residual network (ResNet), which represents the state-of-the-art convolutional neural networks (CNNs). We employ the idea of converting a trained ResNet to a network of spiking neurons named Spiking ResNet. To address the conversion problem, we propose a shortcut normalisation mechanism to appropriately scale continuous-valued activations to match firing rates in SNN, and a layer-wise error compensation approach to reduce the error caused by discretisation. Experimental results on MNIST, CIFAR-10, and CIFAR-100 demonstrate that the proposed Spiking ResNet yields the state-of-the-art performance of SNNs.

Connected through hyperedge HE-1 (induced by source paper "a biologically plausible supervised learning method for spiking neural networks using the symmetric stdp rule"):
  with members:
    - "deep learning in spiking neural networks"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "lstm a search space odyssey"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "bp stdp approximating backpropagation using spike timing dependent plasticity"
    - "conversion of artificial recurrent neural networks to spiking neural networks for low power neuromorphic hardware"
    - "training deep spiking neural networks using backpropagation"

Connected through hyperedge HE-2 (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"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"

Connected through hyperedge HE-3 (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"
    - "direct training for spiking neural networks faster larger better"
    - "slayer spike layer error reassignment in time"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-4 (induced by source paper "deep spiking neural networks for large vocabulary automatic speech recognition"):
  with members:
    - "a tandem learning rule for efficient and rapid inference on deep spiking neural networks"
    - "an efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks"
    - "deep learning in spiking neural networks"
    - "surrogate gradient learning in spiking neural networks"
    - "comprehensive snn compression using admm optimization and activity regularization"
    - "espnet end to end speech processing toolkit"
    - "acoustic and textual data augmentation for improved asr of code switching speech"
    - "going deeper in spiking neural networks vgg and residual architectures"

Connected through hyperedge HE-5 (induced by source paper "temporal coding in spiking neural networks with alpha synaptic function"):
  with members:
    - "deep 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"
    - "evolutionary neural hybrid agents for architecture search"
    - "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"
    - "event driven random back propagation enabling neuromorphic deep learning machines"

Connected through hyperedge HE-6 (induced by source paper "deep spiking neural network with spike count based learning rule"):
  with members:
    - "deep learning in spiking neural networks"
    - "is neuromorphic mnist neuromorphic analyzing the discriminative power of neuromorphic datasets in the time domain"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "direct training for spiking neural networks faster larger better"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "slayer spike layer error reassignment in time"
    - "deep spiking networks"

Connected through hyperedge HE-7 (induced by source paper "restocnet residual stochastic binary convolutional spiking neural network for memory efficient neuromorphic computing"):
  with members:
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "resparc a reconfigurable and energy efficient architecture with memristive crossbars for deep spiking neural networks"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "unsupervised regenerative learning of hierarchical features in spiking deep networks for object recognition"
    - "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems"
    - "supervised learning based on temporal coding in spiking neural networks"

Connected through hyperedge HE-8 (induced by source paper "natcsnn a convolutional spiking neural network for recognition of objects extracted from natural images"):
  with members:
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "deep residual learning for image recognition"
    - "training very deep networks"
    - "striving for simplicity the all convolutional net"

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)