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: "is neuromorphic mnist neuromorphic analyzing the discriminative power of neuromorphic datasets in the time domain"
  Abstract: The advantage of spiking neural networks (SNNs) over their predecessors is their ability to spike, enabling them to use spike timing for coding and efficient computing. A neuromorphic dataset should allow a neuromorphic algorithm to clearly show that a SNN is able to perform better on the dataset than an ANN. We have analyzed both N-MNIST and N-Caltech101 along these lines, but focus our study on N-MNIST. First we evaluate if additional information is encoded in the time domain in a neuromoprhic dataset. We show that an ANN trained with backpropagation on frame based versions of N-MNIST and N-Caltech101 images achieve 99.23% and 78.01% accuracy. These are the best classification accuracies obtained on these datasets to date. Second we present the first unsupervised SNN to be trained on N-MNIST and demonstrate results of 91.78%. We also use this SNN for further experiments on N-MNIST to show that rate based SNNs perform better, and precise spike timings are not important in N-MNIST. N-MNIST does not, therefore, highlight the unique ability of SNNs. The conclusion of this study opens an important question in neuromorphic engineering - what, then, constitutes a good neuromorphic dataset?

Connected through hyperedge HE-1 (induced by source paper "deep spiking neural network with spike count based learning rule"):
  with members:
    - "deep learning in spiking neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "spiking deep residual network"
    - "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-2 (induced by source paper "motion equivariant networks for event cameras with the temporal normalization transform"):
  with members:
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "unsupervised learning of dense optical flow depth and egomotion from sparse event data"
    - "ev segnet semantic segmentation for event based cameras"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "polar transformer networks"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "learning so 3 equivariant representations with spherical cnns"
    - "dynamic steerable blocks in deep residual networks"

Connected through hyperedge HE-3 (induced by source paper "an efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks"):
  with members:
    - "slayer spike layer error reassignment in time"
    - "a spiking network that learns to extract spike signatures from speech signals"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "coco text dataset and benchmark for text detection and recognition in natural images"
    - "converting static image datasets to spiking neuromorphic datasets using saccades"
    - "you only look once unified real time object detection"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-4 (induced by source paper "direct training for spiking neural networks faster larger better"):
  with members:
    - "deep learning in spiking neural networks"
    - "l1 norm batch normalization for efficient training of deep neural networks"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "spiking deep residual network"
    - "hats histograms of averaged time surfaces for robust event based object classification"
    - "bindsnet a machine learning oriented spiking neural networks library in python"
    - "spatio temporal backpropagation for training high performance 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)