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: "perforatedcnns acceleration through elimination of redundant convolutions"
  Abstract: We propose a novel approach to reduce the computational cost of evaluation of convolutional neural networks, a factor that has hindered their deployment in low-power devices such as mobile phones. Inspired by the loop perforation technique from source code optimization, we speed up the bottleneck convolutional layers by skipping their evaluation in some of the spatial positions. We propose and analyze several strategies of choosing these positions. We demonstrate that perforation can accelerate modern convolutional networks such as AlexNet and VGG-16 by a factor of 2x - 4x. Additionally, we show that perforation is complementary to the recently proposed acceleration method of Zhang et al.

Connected through hyperedge HE-1 (induced by source paper "centripetal sgd for pruning very deep convolutional networks with complicated structure"):
  with members:
    - "towards compact convnets via structure sparsity regularized filter pruning"
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "leveraging filter correlations for deep model compression"
    - "discrimination aware channel pruning for deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "structured pruning for efficient convnets via incremental regularization"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-2 (induced by source paper "efficient segmentation learning downsampling near semantic boundaries"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "beyond gradient descent for regularized segmentation losses"
    - "active convolution learning the shape of convolution for image classification"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-3 (induced by source paper "global sparse momentum sgd for pruning very deep neural networks"):
  with members:
    - "metapruning meta learning for automatic neural network channel pruning"
    - "towards compact convnets via structure sparsity regularized filter pruning"
    - "approximated oracle filter pruning for destructive cnn width optimization"
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"

Connected through hyperedge HE-4 (induced by source paper "dynamic multi path neural network"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "slimmable neural networks"
    - "model compression via distillation and quantization"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "quantization mimic towards very tiny cnn for object detection"
    - "skipnet learning dynamic routing in convolutional networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

Connected through hyperedge HE-5 (induced by source paper "thanks for nothing predicting zero valued activations with lightweight convolutional neural networks"):
  with members:
    - "divide and conquer leveraging intermediate feature representations for quantized training of neural networks"
    - "spatial correlation and value prediction in convolutional neural networks"
    - "efficient and robust parallel dnn training through model parallelism on multi gpu platform"
    - "mnasnet platform aware neural architecture search for mobile"
    - "dynamic channel pruning feature boosting and suppression"
    - "an analysis of deep neural network models for practical applications"
    - "sparsenn an energy efficient neural network accelerator exploiting input and output sparsity"
    - "efficient processing of deep neural networks a tutorial and survey"

Connected through hyperedge HE-6 (induced by source paper "dynamic neural network channel execution for efficient training"):
  with members:
    - "faster gaze prediction with dense networks and fisher pruning"
    - "discrimination aware channel pruning for deep neural networks"
    - "layer compensated pruning for resource constrained convolutional neural networks"
    - "gaternet dynamic filter selection in convolutional neural network via a dedicated global gating network"
    - "skipnet learning dynamic routing in convolutional networks"
    - "learning efficient convolutional networks through network slimming"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "structured probabilistic pruning for convolutional neural network acceleration"

Connected through hyperedge HE-7 (induced by source paper "reduced order modeling of deep neural networks"):
  with members:
    - "musco multi stage compression of neural networks"
    - "discrimination aware channel pruning for deep neural networks"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "learning efficient convolutional networks through network slimming"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "channel pruning for accelerating very deep neural networks"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "wide residual networks"

Connected through hyperedge HE-8 (induced by source paper "targeted kernel networks faster convolutions with attentive regularization"):
  with members:
    - "sbnet sparse blocks network for fast inference"
    - "non local neural networks"
    - "dynamic routing between capsules"
    - "eac net a region based deep enhancing and cropping approach for facial action unit detection"
    - "interpretable convolutional neural networks"
    - "attentional pooling for action recognition"
    - "more is less a more complicated network with less inference complexity"
    - "attentive recurrent comparators"

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)