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: "training deep neural networks for binary communication with the whetstone method"
  Abstract: The computational cost of deep neural networks presents challenges to broadly deploying these algorithms. Low-power and embedded neuromorphic processors offer potentially dramatic performance-per-watt improvements over traditional processors. However, programming these brain-inspired platforms generally requires platform-specific expertise. It is therefore difficult to achieve state-of-the-art performance on these platforms, limiting their applicability. Here we present Whetstone, a method to bridge this gap by converting deep neural networks to have discrete, binary communication. During the training process, the activation function at each layer is progressively sharpened towards a threshold activation, with limited loss in performance. Whetstone sharpened networks do not require a rate code or other spike-based coding scheme, thus producing networks comparable in timing and size to conventional artificial neural networks. We demonstrate Whetstone on a number of architectures and tasks such as image classification, autoencoders and semantic segmentation. Whetstone is currently implemented within the Keras wrapper for TensorFlow and is widely extendable. Neuromorphic processors promise to be a low-powered platform for deep learning, but require neural networks that are adapted for binary communication. The Whetstone method achieves this by gradually sharpening activation functions during the training process.

Connected through hyperedge HE-1 (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"
    - "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"
    - "asp learning to forget with adaptive synaptic plasticity in spiking neural networks"

Connected through hyperedge HE-2 (induced by source paper "spikegrad an ann equivalent computation model for implementing backpropagation with spikes"):
  with members:
    - "deep spiking neural network with spike count based learning rule"
    - "a spiking network for inference of relations trained with neuromorphic backpropagation"
    - "deep learning in spiking neural networks"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "direct training for spiking neural networks faster larger better"
    - "long short term memory and learning to learn in networks of spiking neurons"

Connected through hyperedge HE-3 (induced by source paper "dashnet a hybrid artificial and spiking neural network for high speed object tracking"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "pred18 dataset and further experiments with davis event camera in predator prey robot chasing"
    - "direct training for spiking neural networks faster larger better"
    - "context aware synthesis for video frame interpolation"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "video frame interpolation via adaptive convolution"
    - "video frame interpolation via adaptive separable convolution"

Connected through hyperedge HE-4 (induced by source paper "composing neural algorithms with fugu"):
  with members:
    - "spike based primitives for graph algorithms"
    - "spiking neural algorithms for markov process random walk"

Connected through hyperedge HE-5 (induced by source paper "making bread biomimetic strategies for artificial intelligence now and in the future"):
  with members:
    - "spiking neural algorithms for markov process random walk"
    - "in datacenter performance analysis of a tensor processing unit"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "training spiking deep networks for neuromorphic hardware"
    - "stick spike time interval computational kernel a framework for general purpose computation using neurons precise timing delays and synchrony"

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)