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: "unsupervised regenerative learning of hierarchical features in spiking deep networks for object recognition"
  Abstract: We present a spike-based unsupervised regenerative learning scheme to train Spiking Deep Networks (SpikeCNN) for object recognition problems using biologically realistic leaky integrate-and-fire neurons. The training methodology is based on the Auto-Encoder learning model wherein the hierarchical network is trained layer wise using the encoder-decoder principle. Regenerative learning uses spike-timing information and inherent latencies to update the weights and learn representative levels for each convolutional layer in an unsupervised manner. The features learnt from the final layer in the hierarchy are then fed to an output layer. The output layer is trained with supervision by showing a fraction of the labeled training dataset and performs the overall classification of the input. Our proposed methodology yields 0.92%/29.84% classification error on MNIST/CIFAR10 datasets which is comparable with state-of-the-art results. The proposed methodology also introduces sparsity in the hierarchical feature representations on account of event-based coding resulting in computationally efficient learning.

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"
    - "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"
    - "benchmarking keyword spotting efficiency on neuromorphic hardware"

Connected through hyperedge HE-2 (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"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "a spiking network that learns to extract spike signatures from speech signals"
    - "event driven random back propagation enabling neuromorphic deep learning machines"

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"
    - "spiking deep residual network"
    - "direct training for spiking neural networks faster larger better"
    - "slayer spike layer error reassignment in time"

Connected through hyperedge HE-4 (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"
    - "direct training for spiking neural networks faster larger better"
    - "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"

Connected through hyperedge HE-5 (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"

Connected through hyperedge HE-6 (induced by source paper "towards scalable efficient and accurate deep spiking neural networks with backward residual connections stochastic softmax and hybridization"):
  with members:
    - "restocnet residual stochastic binary convolutional spiking neural network for memory efficient neuromorphic computing"
    - "surrogate gradient learning in spiking neural networks"
    - "training deep neural networks for binary communication with the whetstone method"
    - "enabling spike based backpropagation in state of the art deep neural network architectures"
    - "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"
    - "encoding neural and synaptic functionalities in electron spin a pathway to efficient neuromorphic computing"

Connected through hyperedge HE-7 (induced by source paper "optimizing the energy consumption of spiking neural networks for neuromorphic applications"):
  with members:
    - "surrogate gradient learning in spiking neural networks"
    - "energy and policy considerations for deep learning in nlp"
    - "large scale neuromorphic spiking array processors a quest to mimic the brain"
    - "a survey on methods and theories of quantized neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "slayer spike layer error reassignment in time"
    - "pruning convolutional neural networks for resource efficient inference"

Connected through hyperedge HE-8 (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"
    - "spiking deep residual network"
    - "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"
    - "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems"
    - "supervised learning based on temporal coding in spiking neural networks"

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)