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: "multi layered spiking neural network with target timestamp threshold adaptation and stdp"
  Abstract: Spiking neural networks (SNNs) are good candidates to produce ultra-energy-efficient hardware. However, the performance of these models is currently behind traditional methods. Introducing multi-layered SNNs is a promising way to reduce this gap. We propose in this paper a new threshold adaptation system which uses a timestamp objective at which neurons should fire. We show that our method leads to state-of-the-art classification rates on the MNIST dataset (98.60%) and the Faces/Motorbikes dataset (99.46%) with an unsupervised SNN followed by a linear SVM. We also investigate the sparsity level of the network by testing different inhibition policies and STDP rules.

Connected through hyperedge HE-1 (induced by source paper "spiking inception module for multi layer unsupervised spiking neural networks"):
  with members:
    - "biosnet a fast learning and high robustness unsupervised biomimetic spiking neural network"
    - "locally connected spiking neural networks for unsupervised feature learning"
    - "deep learning in spiking neural networks"
    - "unsupervised learning with self organizing spiking neural networks"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "asp learning to forget with adaptive synaptic plasticity in spiking neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-2 (induced by source paper "biosnet a fast learning and high robustness unsupervised biomimetic spiking neural network"):
  with members:
    - "locally connected spiking neural networks for unsupervised feature learning"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "asp learning to forget with adaptive synaptic plasticity in spiking neural networks"
    - "emnist an extension of mnist to handwritten letters"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "s4nn temporal backpropagation for spiking neural networks with one spike per neuron"):
  with members:
    - "recognizing images with at most one spike per neuron"
    - "biologically plausible deep learning but how far can we go with shallow networks"
    - "spikegrad an ann equivalent computation model for implementing backpropagation with spikes"
    - "deep convolutional spiking neural networks for image classification"
    - "deep learning in spiking neural networks"
    - "fast and deep neuromorphic learning with time to first spike coding"
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "surrogate gradient learning in spiking neural networks"

Connected through hyperedge HE-4 (induced by source paper "bio inspired digit recognition using reward modulated spike timing dependent plasticity in deep convolutional networks"):
  with members:
    - "deep convolutional spiking neural networks for image classification"
    - "deep learning in spiking neural networks"
    - "spyketorch efficient simulation of convolutional spiking neural networks with at most one spike per neuron"
    - "online learning a comprehensive survey"
    - "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"
    - "spatio temporal backpropagation for training high performance spiking neural networks"

Connected through hyperedge HE-5 (induced by source paper "spyketorch efficient simulation of convolutional spiking neural networks with at most one spike per neuron"):
  with members:
    - "deep convolutional spiking neural networks for image classification"
    - "deep learning in spiking neural networks"
    - "surrogate gradient learning in spiking neural networks"
    - "bio inspired digit recognition using reward modulated spike timing dependent plasticity in deep convolutional networks"
    - "mt spike a multilayer time based spiking neuromorphic architecture with temporal error backpropagation"
    - "bindsnet a machine learning oriented spiking neural networks library in python"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "long short term memory and learning to learn in networks of spiking neurons"

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.CV(Computer Vision and Pattern Recognition)