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: "prune the convolutional neural networks with sparse shrink"
  Abstract: Nowadays, it is still difficult to adapt Convolutional Neural Network (CNN) based models for deployment on embedded devices. The heavy computation and large memory footprint of CNN models become the main burden in real application. In this paper, we propose a "Sparse Shrink" algorithm to prune an existing CNN model. By analyzing the importance of each channel via sparse reconstruction, the algorithm is able to prune redundant feature maps accordingly. The resulting pruned model thus directly saves computational resource. We have evaluated our algorithm on CIFAR-100. As shown in our experiments, we can reduce 56.77% parameters and 73.84% multiplication in total with only minor decrease in accuracy. These results have demonstrated the effectiveness of our "Sparse Shrink" algorithm.

Connected through hyperedge HE-1 (induced by source paper "convolutional neural network pruning using filter attenuation"):
  with members:
    - "modeling of pruning techniques for deep neural networks simplification"
    - "approximated oracle filter pruning for destructive cnn width optimization"
    - "learning to prune filters in convolutional neural networks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "thinet a filter level pruning method for deep neural network compression"
    - "channel pruning for accelerating very deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "a novel channel pruning method for deep neural network compression"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "learning to prune deep neural networks via layer wise optimal brain surgeon"
    - "channel pruning for accelerating very deep neural networks"
    - "aggregated residual transformations for deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "network trimming a data driven neuron pruning approach towards efficient deep architectures"

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)