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: "direct training based spiking convolutional neural networks for object recognition"
  Abstract: Direct training based spiking neural networks (SNNs) have been paid a lot of attention recently because of its high energy efficiency on emerging neuromorphic hardware. However, due to the non-differentiability of the spiking activity, most of the related SNNs still cannot achieve high object recognition accuracy for the complicated dataset, such as CIFAR-10. Even though some of them can reach the accuracy of 90%, the energy consumption in those networks is very high. Considering this, we propose a direct supervised learning based spiking convolutional neural networks (SCNNs) using temporal coding scheme in this study, aiming to exploit minimum trainable parameters to recognize the object in the image with high accuracy. The MNIST and CIFAR-10 datasets are used to evaluate the performance of the proposed networks. For the MNIST dataset, the proposed networks with noise input are able to reach the high recognition accuracy (99.13%) as the other state-of-art models but use the much less trainable parameters than them. For CIFAR-10 dataset, the proposed networks with data augmentation step can reach the recognition accuracy of 80.49%., which is the state-of-art high accuracy in the field of direct training based SNNs using temporal coding manner. In addition, the number of trainable parameters used in such networks is much less than that in the conversion based SCNNs reported in the literature.

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)