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: "representation learning using event based stdp"
  Abstract: Abstract   Although representation learning methods developed within the framework of traditional neural networks are relatively mature, developing a spiking representation model remains a challenging problem. This paper proposes an event-based method to train a feedforward spiking neural network (SNN) layer for extracting visual features. The method introduces a novel spike-timing-dependent plasticity (STDP) learning rule and a threshold adjustment rule both derived from a vector quantization-like objective function subject to a sparsity constraint. The STDP rule is obtained by the gradient of a vector quantization criterion that is converted to spike-based, spatio-temporally local update rules in a spiking network of leaky, integrate-and-fire (LIF) neurons. Independence and sparsity of the model are achieved by the threshold adjustment rule and by a softmax function implementing inhibition in the representation layer consisting of WTA-thresholded spiking neurons. Together, these mechanisms implement a form of spike-based, competitive learning. Two sets of experiments are performed on the MNIST and natural image datasets. The results demonstrate a sparse spiking visual representation model with low reconstruction loss comparable with state-of-the-art visual coding approaches, yet our rule is local in both time and space, thus biologically plausible and hardware friendly.

Connected through hyperedge HE-1 (induced by source paper "deep learning in spiking neural networks"):
  with members:
    - "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"
    - "bp stdp approximating backpropagation using spike timing dependent plasticity"

Connected through hyperedge HE-2 (induced by source paper "synthesizing images from spatio temporal representations using spike based backpropagation"):
  with members:
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "direct training for spiking neural networks faster larger better"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "slayer spike layer error reassignment in time"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "unsupervised regenerative learning of hierarchical features in spiking deep networks for object recognition"
    - "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"
    - "training deep spiking neural networks using backpropagation"

Connected through hyperedge HE-3 (induced by source paper "bp stdp approximating backpropagation using spike timing dependent plasticity"):
  with members:
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "a spiking network that learns to extract spike signatures from speech signals"
    - "bio inspired spiking convolutional neural network using layer wise sparse coding and stdp learning"
    - "deep spiking networks"
    - "supervised learning based on temporal coding in spiking neural networks"
    - "training deep spiking neural networks using backpropagation"
    - "event driven contrastive divergence for spiking neuromorphic systems"

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)