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: "cop customized deep model compression via regularized correlation based filter level pruning"
  Abstract: Neural network compression empowers the effective yet unwieldy deep convolutional neural networks (CNN) to be deployed in resource-constrained scenarios. Most state-of-the-art approaches prune the model in filter-level according to the "importance" of filters. Despite their success, we notice they suffer from at least two of the following problems: 1) The redundancy among filters is not considered because the importance is evaluated independently. 2) Cross-layer filter comparison is unachievable since the importance is defined locally within each layer. Consequently, we must manually specify layer-wise pruning ratios. 3) They are prone to generate sub-optimal solutions because they neglect the inequality between reducing parameters and reducing computational cost. Reducing the same number of parameters in different positions in the network may reduce different computational cost. To address the above problems, we develop a novel algorithm named as COP (correlation-based pruning), which can detect the redundant filters efficiently. We enable the cross-layer filter comparison through global normalization. We add parameter-quantity and computational-cost regularization terms to the importance, which enables the users to customize the compression according to their preference (smaller or faster). Extensive experiments have shown COP outperforms the others significantly. The code is released at this https URL.

Connected through hyperedge HE-1 (induced by source paper "channel pruning via automatic structure search"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "play and prune adaptive filter pruning for deep model compression"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "amc automl for model compression and acceleration on mobile devices"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "haq hardware aware automated quantization with mixed precision"
    - "soft filter pruning for accelerating deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "dbp discrimination based block level pruning for deep model acceleration"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "similarity preserving knowledge distillation"
    - "greedy layerwise learning can scale to imagenet"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "knowledge distillation with adversarial samples supporting decision boundary"
    - "adversarial network compression"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-3 (induced by source paper "removable and or repeated units emerge in overparametrized deep neural networks"):
  with members:
    - "kernel and deep regimes in overparametrized models"
    - "intrinsic dimension of data representations in deep neural networks"
    - "measuring the intrinsic dimension of objective landscapes"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "exploring generalization in deep learning"
    - "thinet a filter level pruning method for deep neural network compression"
    - "a pac bayesian approach to spectrally normalized margin bounds for 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.CV(Computer Vision and Pattern Recognition)