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: "filter pruning via geometric median for deep convolutional neural networks acceleration"
  Abstract: Previous works utilized ''smaller-norm-less-important'' criterion to prune filters with smaller norm values in a convolutional neural network. In this paper, we analyze this norm-based criterion and point out that its effectiveness depends on two requirements that are not always met: (1) the norm deviation of the filters should be large; (2) the minimum norm of the filters should be small. To solve this problem, we propose a novel filter pruning method, namely Filter Pruning via Geometric Median (FPGM), to compress the model regardless of those two requirements. Unlike previous methods, FPGM compresses CNN models by pruning filters with redundancy, rather than those with ''relatively less'' importance. When applied to two image classification benchmarks, our method validates its usefulness and strengths. Notably, on CIFAR-10, FPGM reduces more than 52% FLOPs on ResNet-110 with even 2.69% relative accuracy improvement. Moreover, on ILSVRC-2012, FPGM reduces more than 42% FLOPs on ResNet-101 without top-5 accuracy drop, which has advanced the state-of-the-art. Code is publicly available on GitHub: this https URL

Connected through hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression"):
  with members:
    - "hierarchical binary cnns for landmark localization with limited resources"
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "gate decorator global filter pruning method for accelerating deep convolutional neural networks"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "importance estimation for neural network pruning"
    - "nat neural architecture transformer for accurate and compact architectures"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-2 (induced by source paper "towards efficient model compression via learned global ranking"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "regularizing activation distribution for training binarized deep networks"
    - "adascale towards real time video object detection using adaptive scaling"
    - "approximated oracle filter pruning for destructive cnn width optimization"
    - "importance estimation for neural network pruning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mavbench micro aerial vehicle benchmarking"

Connected through hyperedge HE-3 (induced by source paper "highly efficient salient object detection with 100k parameters"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "linear context transform block"
    - "searching for mobilenetv3"
    - "a simple pooling based design for real time salient object detection"
    - "dabnet depth wise asymmetric bottleneck for real time semantic segmentation"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "deeply supervised salient object detection with short connections"

Connected through hyperedge HE-4 (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-5 (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"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-6 (induced by source paper "widening and squeezing towards accurate and efficient qnns"):
  with members:
    - "learning instance wise sparsity for accelerating deep models"
    - "searching for accurate binary neural architectures"
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "bnn improved binary network training"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-7 (induced by source paper "progressive gradient pruning for classification detection and domainadaptation"):
  with members:
    - "importance estimation for neural network pruning"
    - "structured pruning of neural networks with budget aware regularization"
    - "yolov3 an incremental improvement"
    - "sbnet sparse blocks network for fast inference"
    - "discrimination aware channel pruning for deep neural networks"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "progressive deep neural networks acceleration via soft filter pruning"

Connected through hyperedge HE-8 (induced by source paper "sasl saliency adaptive sparsity learning for neural network acceleration"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "oicsr out in channel sparsity regularization for compact deep neural networks"
    - "importance estimation for neural network pruning"
    - "global sparse momentum sgd for pruning very deep neural networks"
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "learning to prune filters in convolutional neural networks"
    - "structured pruning of neural networks with budget aware regularization"
    - "amc automl for model compression and acceleration on mobile devices"

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)