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: "going deeper in spiking neural networks vgg and residual architectures"
  Abstract: Over the past few years, Spiking Neural Networks (SNNs) have become popular as a possible pathway to enable low-power event-driven neuromorphic hardware. However, their application in machine learning have largely been limited to very shallow neural network architectures for simple problems. In this paper, we propose a novel algorithmic technique for generating an SNN with a deep architecture, and demonstrate its effectiveness on complex visual recognition problems such as CIFAR-10 and ImageNet. Our technique applies to both VGG and Residual network architectures, with significantly better accuracy than the state-of-the-art. Finally, we present analysis of the sparse event-driven computations to demonstrate reduced hardware overhead when operating in the spiking domain.

Connected through hyperedge HE-1 (induced by source paper "rmp snns residual membrane potential neuron for enabling deeper high accuracy and low latency spiking neural networks"):
  with members:
    - "restocnet residual stochastic binary convolutional spiking neural network for memory efficient neuromorphic computing"
    - "surrogate gradient learning in spiking neural networks"
    - "enabling spike based backpropagation in state of the art deep neural network architectures"
    - "hybrid macro micro level backpropagation for training deep 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"
    - "benchmarking keyword spotting efficiency on neuromorphic hardware"
    - "slayer spike layer error reassignment in time"

Connected through hyperedge HE-2 (induced by source paper "siamsnn spike based siamese network for energy efficient and real time object tracking"):
  with members:
    - "deep learning in spiking neural networks"
    - "spiking yolo spiking neural network for energy efficient object detection"
    - "target aware deep tracking"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "distractor aware siamese networks for visual object tracking"
    - "fast and efficient information transmission with burst spikes in deep spiking neural networks"
    - "quantization mimic towards very tiny cnn for object detection"
    - "siamrpn evolution of siamese visual tracking with very deep networks"

Connected through hyperedge HE-3 (induced by source paper "recognizing images with at most one spike per neuron"):
  with members:
    - "randaugment practical data augmentation with no separate search"
    - "s4nn temporal backpropagation for spiking neural networks with one spike per neuron"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "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-4 (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"
    - "direct training for spiking neural networks faster larger better"
    - "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-5 (induced by source paper "classifying images with few spikes per neuron"):
  with members:
    - "recognizing images with at most one spike per neuron"
    - "s4nn temporal backpropagation for spiking neural networks with 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"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "sbsnn stochastic bits enabled binary spiking neural network with on chip learning for energy efficient neuromorphic computing at the edge"):
  with members:
    - "enabling spike based backpropagation in state of the art deep neural network architectures"
    - "benchmarking keyword spotting efficiency on neuromorphic hardware"
    - "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems"
    - "a cmos spiking neuron for brain inspired neural networks with resistive synapses and in situ learning"
    - "adam a method for stochastic optimization"
    - "event driven contrastive divergence for spiking neuromorphic systems"

Connected through hyperedge HE-7 (induced by source paper "is my neural network neuromorphic taxonomy recent trends and future directions in neuromorphic engineering"):
  with members:
    - "spiking neural network based region proposal networks for neuromorphic vision sensors"
    - "ebbiot a low complexity tracking algorithm for surveillance in iovt using stationary neuromorphic vision sensors"
    - "real time closed loop neural decoding on a neuromorphic chip"
    - "a 128 channel extreme learning machine based neural decoder for brain machine interfaces"
    - "memory and information processing in neuromorphic systems"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-8 (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"
    - "progressive weight pruning of deep neural networks using admm"
    - "spiking deep residual network"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"

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.CV(Computer Vision and Pattern Recognition)