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: "stdp learning of image patches with convolutional spiking neural networks"
  Abstract: Spiking neural networks are motivated from principles of neural systems and may possess unexplored advantages in the context of machine learning. A class of \textit{convolutional spiking neural networks} is introduced, trained to detect image features with an unsupervised, competitive learning mechanism. Image features can be shared within subpopulations of neurons, or each may evolve independently to capture different features in different regions of input space. We analyze the time and memory requirements of learning with and operating such networks. The MNIST dataset is used as an experimental testbed, and comparisons are made between the performance and convergence speed of a baseline spiking neural network.

Connected through hyperedge HE-1 (induced by source paper "locally connected spiking neural networks for unsupervised feature learning"):
  with members:
    - "deep learning in spiking neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "bindsnet a machine learning oriented spiking neural networks library in python"
    - "unsupervised learning with self organizing spiking neural networks"
    - "quantifying translation invariance in convolutional neural networks"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "efficient computation in adaptive artificial spiking neural networks"
    - "unsupervised regenerative learning of hierarchical features in spiking deep networks for object recognition"

Connected through hyperedge HE-2 (induced by source paper "lattice map spiking neural networks lm snns for clustering and classifying image data"):
  with members:
    - "bindsnet a machine learning oriented spiking neural networks library in python"
    - "unsupervised learning with self organizing spiking neural networks"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "training deep spiking neural networks using backpropagation"
    - "deep learning in 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)