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: "recent advances in efficient computation of deep convolutional neural networks"
  Abstract: Deep neural networks have evolved remarkably over the past few years and they are currently the fundamental tools of many intelligent systems. At the same time, the computational complexity and resource consumption of these networks also continue to increase. This will pose a significant challenge to the deployment of such networks, especially in real-time applications or on resource-limited devices. Thus, network acceleration has become a hot topic within the deep learning community. As for hardware implementation of deep neural networks, a batch of accelerators based on FPGA/ASIC have been proposed in recent years. In this paper, we provide a comprehensive survey of recent advances in network acceleration, compression and accelerator design from both algorithm and hardware points of view. Specifically, we provide a thorough analysis of each of the following topics: network pruning, low-rank approximation, network quantization, teacher-student networks, compact network design and hardware accelerators. Finally, we will introduce and discuss a few possible future directions.

Connected through hyperedge HE-1 (induced by source paper "model agnostic structured sparsification with learnable channel shuffle"):
  with members:
    - "search to distill pearls are everywhere but not the eyes"
    - "differentiable learning to group channels via groupable convolutional neural networks"
    - "importance estimation for neural network pruning"
    - "fully learnable group convolution for acceleration of deep neural networks"
    - "pytorch an imperative style high performance deep learning library"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"

Connected through hyperedge HE-2 (induced by source paper "hrank filter pruning using high rank feature map"):
  with members:
    - "filter sketch for network pruning"
    - "channel pruning via automatic structure search"
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "revisiting the importance of individual units in cnns via ablation"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"
    - "data driven sparse structure selection for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "lance efficient low precision quantized winograd convolution for neural networks based on graphics processing units"):
  with members:
    - "jointly sparse convolutional neural networks in dual spatial winograd domains"
    - "efficient winograd or cook toom convolution kernel implementation on widely used mobile cpus"
    - "a survey on methods and theories of quantized 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"

Connected through hyperedge HE-4 (induced by source paper "class feature pyramids for video explanation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "video classification with channel separated convolutional networks"
    - "learning spatio temporal representation with local and global diffusion"
    - "saliency tubes visual explanations for spatio temporal convolutions"
    - "large scale weakly supervised pre training for video action recognition"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "multigrain a unified image embedding for classes and instances"
    - "grounding visual explanations"

Connected through hyperedge HE-5 (induced by source paper "bringing giant neural networks down to earth with unlabeled data"):
  with members:
    - "importance estimation for neural network pruning"
    - "from hashing to cnns training binaryweight networks via hashing"
    - "deep predictive coding network for object recognition"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "discrimination aware channel pruning for deep neural networks"
    - "exploring the limits of weakly supervised pretraining"
    - "rethinking the value of network pruning"
    - "dynamic channel pruning feature boosting and suppression"

Connected through hyperedge HE-6 (induced by source paper "how to manipulate cnns to make them lie the gradcam case"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "taking human out of learning applications a survey on automated machine learning"
    - "sok security and privacy in machine learning"
    - "sanity checks for saliency maps"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"

Connected through hyperedge HE-7 (induced by source paper "compactnet platform aware automatic optimization for convolutional neural networks"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "morphnet fast simple resource constrained structure learning of deep networks"
    - "exploring the regularity of sparse structure in convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "accelerating cnn training by sparsifying activation gradients"):
  with members:
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "repr improved training of convolutional filters"
    - "value aware quantization for training and inference of neural networks"
    - "imagenet training in minutes"
    - "learning efficient convolutional networks through network slimming"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "sparse communication for distributed gradient descent"
    - "exploring the regularity of sparse structure in convolutional 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)