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: "jointly sparse convolutional neural networks in dual spatial winograd domains"
  Abstract: We consider the optimization of deep convolutional neural networks (CNNs) such that they provide good performance while having reduced complexity if deployed on either conventional systems with spatial-domain convolution or lower-complexity systems designed for Winograd convolution. The proposed framework produces one compressed model whose convolutional filters can be made sparse either in the spatial domain or in the Winograd domain. Hence, the compressed model can be deployed universally on any platform, without need for re-training on the deployed platform. To get a better compression ratio, the sparse model is compressed in the spatial domain that has a fewer number of parameters. From our experiments, we obtain $24.2\times$ and $47.7\times$ compressed models for ResNet-18 and AlexNet trained on the ImageNet dataset, while their computational cost is also reduced by $4.5\times$ and $5.1\times$, respectively.

Connected through hyperedge HE-1 (induced by source paper "what is the state of neural network pruning"):
  with members:
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "benchmark analysis of representative deep neural network architectures"
    - "sprintz time series compression for the internet of things"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "efficient neural audio synthesis"
    - "network compression using correlation analysis of layer responses"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "extreme network compression via filter group approximation"

Connected through hyperedge HE-2 (induced by source paper "lance efficient low precision quantized winograd convolution for neural networks based on graphics processing units"):
  with members:
    - "efficient winograd or cook toom convolution kernel implementation on widely used mobile cpus"
    - "a survey on methods and theories of quantized neural networks"
    - "recent advances in efficient computation of deep convolutional neural networks"
    - "spatial winograd pruning enabling sparse winograd convolution"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "ternary weight networks"
    - "quantized neural networks training neural networks with low precision weights and activations"

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)