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: "compact deep convolutional neural networks with coarse pruning"
  Abstract: The learning capability of a neural network improves with increasing depth at higher computational costs. Wider layers with dense kernel connectivity patterns furhter increase this cost and may hinder real-time inference. We propose feature map and kernel level pruning for reducing the computational complexity of a deep convolutional neural network. Pruning feature maps reduces the width of a layer and hence does not need any sparse representation. Further, kernel pruning converts the dense connectivity pattern into a sparse one. Due to coarse nature, these pruning granularities can be exploited by GPUs and VLSI based implementations. We propose a simple and generic strategy to choose the least adversarial pruning masks for both granularities. The pruned networks are retrained which compensates the loss in accuracy. We obtain the best pruning ratios when we prune a network with both granularities. Experiments with the CIFAR-10 dataset show that more than 85% sparsity can be induced in the convolution layers with less than 1% increase in the missclassification rate of the baseline network.

Connected through hyperedge HE-1 (induced by source paper "cluster pruning an efficient filter pruning method for edge ai vision applications"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "bigdl a distributed deep learning framework for big data"
    - "network decoupling from regular to depthwise separable convolutions"
    - "deep neural network compression with single and multiple level quantization"

Connected through hyperedge HE-2 (induced by source paper "depth wise decomposition for accelerating separable convolutions in efficient convolutional neural networks"):
  with members:
    - "mobinet a mobile binary network for image classification"
    - "pack and detect fast object detection in videos using region of interest packing"
    - "feature selective anchor free module for single shot object detection"
    - "prediction tracking segmentation"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bounding box regression with uncertainty for accurate object detection"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "haq hardware aware automated quantization with mixed precision"

Connected through hyperedge HE-3 (induced by source paper "metapruning meta learning for automatic neural network channel pruning"):
  with members:
    - "meta sr a magnification arbitrary network for super resolution"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "metaanchor learning to detect objects with customized anchors"
    - "slimmable neural networks"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "amc automl for model compression and acceleration on mobile devices"
    - "dpp net device aware progressive search for pareto optimal neural architectures"

Connected through hyperedge HE-4 (induced by source paper "a programmable approach to model compression"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "discrimination aware channel pruning for deep neural networks"
    - "progressive deep neural networks acceleration via soft filter pruning"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "attention is all you need"
    - "model compression as constrained optimization with application to neural nets part i general framework"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-5 (induced by source paper "a one step pruning recovery framework for acceleration of convolutional neural networks"):
  with members:
    - "exploring linear relationship in feature map subspace for convnets compression"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "learning intrinsic sparse structures within long short term memory"
    - "thinet a filter level pruning method for deep neural network compression"
    - "channel pruning for accelerating very deep neural networks"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-6 (induced by source paper "compressing complex convolutional neural network based on an improved deep compression algorithm"):
  with members:
    - "efficient sparse winograd convolutional neural networks"
    - "deep quaternion networks"
    - "deep complex networks"
    - "enabling sparse winograd convolution by native pruning"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "exploiting linear structure within convolutional networks for efficient evaluation"

Connected through hyperedge HE-7 (induced by source paper "rethinking the value of network pruning"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "network compression using correlation analysis of layer responses"
    - "darts differentiable architecture search"
    - "layer compensated pruning for resource constrained convolutional neural networks"
    - "soft filter pruning for accelerating deep convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "amc automl for model compression and acceleration on mobile devices"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "domain adaptive deep network compression"
    - "an analysis of deep neural network models for practical applications"
    - "learning transferable architectures for scalable image recognition"
    - "structured probabilistic pruning for convolutional neural network acceleration"

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.LG(Machine Learning)