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: "soft filter pruning for accelerating deep convolutional neural networks"
  Abstract: This paper proposed a Soft Filter Pruning (SFP) method to accelerate the inference procedure of deep Convolutional Neural Networks (CNNs). Specifically, the proposed SFP enables the pruned filters to be updated when training the model after pruning. SFP has two advantages over previous works: (1) Larger model capacity. Updating previously pruned filters provides our approach with larger optimization space than fixing the filters to zero. Therefore, the network trained by our method has a larger model capacity to learn from the training data. (2) Less dependence on the pre-trained model. Large capacity enables SFP to train from scratch and prune the model simultaneously. In contrast, previous filter pruning methods should be conducted on the basis of the pre-trained model to guarantee their performance. Empirically, SFP from scratch outperforms the previous filter pruning methods. Moreover, our approach has been demonstrated effective for many advanced CNN architectures. Notably, on ILSCRC-2012, SFP reduces more than 42% FLOPs on ResNet-101 with even 0.2% top-5 accuracy improvement, 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 "a network pruning network approach to deep model compression"):
  with members:
    - "cooperative initialization based deep neural network training"
    - "accuracy booster performance boosting using feature map re calibration"
    - "cpwc contextual point wise convolution for object recognition"
    - "play and prune adaptive filter pruning for deep model compression"
    - "stability based filter pruning for accelerating deep cnns"
    - "multi layer pruning framework for compressing single shot multibox detector"
    - "amc automl for model compression and acceleration on mobile devices"
    - "leveraging filter correlations for deep model compression"

Connected through hyperedge HE-5 (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"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "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"
    - "bnn improved binary network training"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-6 (induced by source paper "progressive gradient pruning for classification detection and domainadaptation"):
  with members:
    - "importance estimation for neural network pruning"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "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"

Connected through hyperedge HE-7 (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"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "structured pruning of neural networks with budget aware regularization"

Connected through hyperedge HE-8 (induced by source paper "good subnetworks provably exist pruning via greedy forward selection"):
  with members:
    - "proving the lottery ticket hypothesis pruning is all you need"
    - "pruning from scratch"
    - "sipping neural networks sensitivity informed provable pruning of neural networks"
    - "searching for mobilenetv3"
    - "data independent neural pruning via coresets"
    - "legr filter pruning via learned global ranking"
    - "what s hidden in a randomly weighted neural network"
    - "provable filter pruning for efficient 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)