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: "exploiting kernel sparsity and entropy for interpretable cnn compression"
  Abstract: Compressing convolutional neural networks (CNNs) has received ever-increasing research focus. However, most existing CNN compression methods do not interpret their inherent structures to distinguish the implicit redundancy. In this paper, we investigate the problem of CNN compression from a novel interpretable perspective. The relationship between the input feature maps and 2D kernels is revealed in a theoretical framework, based on which a kernel sparsity and entropy (KSE) indicator is proposed to quantitate the feature map importance in a feature-agnostic manner to guide model compression. Kernel clustering is further conducted based on the KSE indicator to accomplish high-precision CNN compression. KSE is capable of simultaneously compressing each layer in an efficient way, which is significantly faster compared to previous data-driven feature map pruning methods. We comprehensively evaluate the compression and speedup of the proposed method on CIFAR-10, SVHN and ImageNet 2012. Our method demonstrates superior performance gains over previous ones. In particular, it achieves 4.7 \times FLOPs reduction and 2.9 \times compression on ResNet-50 with only a Top-5 accuracy drop of 0.35% on ImageNet 2012, which significantly outperforms state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "group sparsity the hinge between filter pruning and decomposition for network compression"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "3d appearance super resolution with deep learning"
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "oicsr out in channel sparsity regularization for compact deep neural networks"
    - "gasl guided attention for sparsity learning in deep neural networks"
    - "importance estimation for neural network pruning"
    - "learning filter basis for convolutional neural network compression"
    - "similarity preserving knowledge distillation"

Connected through hyperedge HE-2 (induced by source paper "learning filter basis for convolutional neural network compression"):
  with members:
    - "3d appearance super resolution with deep learning"
    - "cascaded projection end to end network compression and acceleration"
    - "amc automl for model compression and acceleration on mobile devices"
    - "efficient neural network compression"
    - "multi bin trainable linear unit for fast image restoration networks"
    - "extreme network compression via filter group approximation"
    - "learning efficient convolutional networks through network slimming"
    - "enhanced deep residual networks for single image super resolution"

Connected through hyperedge HE-3 (induced by source paper "utility analysis of network architectures for 3d point cloud processing"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a cnn annularly convolutional neural networks on point clouds"
    - "similarity of neural network representations revisited"
    - "quantifying layerwise information discarding of neural networks"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "3d point capsule networks"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "multiresolution tree networks for 3d point cloud processing"

Connected through hyperedge HE-4 (induced by source paper "specnet spectral domain convolutional neural network"):
  with members:
    - "efficient memory management for gpu based deep learning systems"
    - "sort second order response transform for visual recognition"
    - "memory efficient implementation of densenets"
    - "wavelet convolutional neural networks for texture classification"
    - "the power of sparsity in convolutional neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "learning structured sparsity in deep 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)