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: "spatio temporal backpropagation for training high performance spiking neural networks"
  Abstract: Spiking neural networks (SNNs) are promising in ascertaining brain-like behaviors since spikes are capable of encoding spatio-temporal information. Recent schemes, e.g. pre-training from artificial neural networks (ANNs) or direct training based on backpropagation (BP), make the high-performance supervised training of SNNs possible. However, these methods primarily fasten more attention on its spatial domain information, and the dynamics in temporal domain are attached less significance. Consequently, this might lead to the performance bottleneck, and scores of training techniques shall be additionally required. Another underlying problem is that the spike activity is naturally non-differentiable, raising more difficulties in supervised training of SNNs. In this paper, we propose a spatio-temporal backpropagation (STBP) algorithm for training high-performance spiking neural networks. In order to solve the non-differentiable problem of SNNs, an approximated derivative for spike activity is proposed, being appropriate for gradient descent training. The STBP algorithm combines the layer-by-layer spatial domain (SD) and the timing-dependent temporal domain (TD), and does not require any additional complicated skill. We evaluate this method through adopting both the fully connected and convolutional architecture on the static MNIST dataset, a custom object detection dataset, and the dynamic N-MNIST dataset. Results bespeak that our approach achieves the best accuracy compared with existing state-of-the-art algorithms on spiking networks. This work provides a new perspective to investigate the high-performance SNNs for future brain-like computing paradigm with rich spatio-temporal dynamics.

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"
    - "direct training for spiking neural networks faster larger better"
    - "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"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-2 (induced by source paper "event based angular velocity regression with spiking networks"):
  with members:
    - "deep learning in spiking neural networks"
    - "end to end learning of representations for asynchronous event based data"
    - "event based vision a survey"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "slayer spike layer error reassignment in time"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"

Connected through hyperedge HE-3 (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"
    - "spiking deep residual network"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "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-4 (induced by source paper "reinforcement co learning of deep and spiking neural networks for energy efficient mapless navigation with neuromorphic hardware"):
  with members:
    - "spiking neural network on neuromorphic hardware for energy efficient unidimensional slam"
    - "slayer spike layer error reassignment in time"
    - "parameter space noise for exploration"
    - "reverse curriculum generation for reinforcement learning"
    - "resparc a reconfigurable and energy efficient architecture with memristive crossbars for deep spiking neural networks"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "conversion of artificial recurrent neural networks to spiking neural networks for low power neuromorphic hardware"

Connected through hyperedge HE-5 (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"
    - "direct training for spiking neural networks faster larger better"
    - "slayer spike layer error reassignment in time"
    - "bp stdp approximating backpropagation using spike timing dependent plasticity"

Connected through hyperedge HE-6 (induced by source paper "event based vision a survey"):
  with members:
    - "event based motion segmentation by motion compensation"
    - "ev imo motion segmentation dataset and learning pipeline for event cameras"
    - "atis spinnaker a fully event based visual tracking demonstration"
    - "ced color event camera dataset"
    - "unsupervised learning of a hierarchical spiking neural network for optical flow estimation from events to global motion perception"
    - "high speed and high dynamic range video with an event camera"
    - "end to end learning of representations for asynchronous event based data"
    - "speed invariant time surface for learning to detect corner points with event based cameras"

Connected through hyperedge HE-7 (induced by source paper "deep learning in spiking neural networks"):
  with members:
    - "representation learning using event based stdp"
    - "mt spike a multilayer time based spiking neuromorphic architecture with temporal error backpropagation"
    - "optimal localist and distributed coding of spatiotemporal spike patterns through stdp and coincidence detection"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "a spiking network that learns to extract spike signatures from speech signals"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "bp stdp approximating backpropagation using spike timing dependent plasticity"

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"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "progressive weight pruning of deep neural networks using admm"
    - "spiking deep residual network"

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)