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: "extreme network compression via filter group approximation"
  Abstract: In this paper we propose a novel decomposition method based on filter group approximation, which can significantly reduce the redundancy of deep convolutional neural networks (CNNs) while maintaining the majority of feature representation. Unlike other low-rank decomposition algorithms which operate on spatial or channel dimension of filters, our proposed method mainly focuses on exploiting the filter group structure for each layer. For several commonly used CNN models, including VGG and ResNet, our method can reduce over 80% floating-point operations (FLOPs) with less accuracy drop than state-of-the-art methods on various image classification datasets. Besides, experiments demonstrate that our method is conducive to alleviating degeneracy of the compressed network, which hurts the convergence and performance of the network.

Connected through hyperedge HE-1 (induced by source paper "lossless compression of deep neural networks"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "caql continuous action q learning"
    - "function approximation by deep networks"
    - "equivalent and approximate transformations of deep neural networks"
    - "differentiable convex optimization layers"
    - "accelerating primal solution findings for mixed integer programs based on solution prediction"
    - "reinforcement learning for integer programming learning to cut"
    - "machine learning for combinatorial optimization a methodological tour d horizon"

Connected through hyperedge HE-2 (induced by source paper "bidet an efficient binarized object detector"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "thundernet towards real time generic object detection"
    - "embarrassingly simple binary representation learning"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "importance estimation for neural network pruning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "efficient neural network compression"

Connected through hyperedge HE-3 (induced by source paper "what is the state of neural network pruning"):
  with members:
    - "jointly sparse convolutional neural networks in dual spatial winograd domains"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "benchmark analysis of representative deep neural network architectures"
    - "sprintz time series compression for the internet of things"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "efficient neural audio synthesis"
    - "network compression using correlation analysis of layer responses"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"

Connected through hyperedge HE-4 (induced by source paper "learning filter basis for convolutional neural network compression"):
  with members:
    - "3d appearance super resolution with deep learning"
    - "cascaded projection end to end network compression and acceleration"
    - "amc automl for model compression and acceleration on mobile devices"
    - "efficient neural network compression"
    - "multi bin trainable linear unit for fast image restoration networks"
    - "exploiting kernel sparsity and entropy for interpretable cnn compression"
    - "learning efficient convolutional networks through network slimming"
    - "enhanced deep residual networks for single image super resolution"

Connected through hyperedge HE-5 (induced by source paper "similarity preserving knowledge distillation"):
  with members:
    - "lifelong gan continual learning for conditional image generation"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "cinic 10 is not imagenet or cifar 10"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "knowledge distillation by on the fly native ensemble"
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-6 (induced by source paper "filter distillation for network compression"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "democratizing production scale distributed deep learning"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"
    - "domain adaptive deep network compression"
    - "thinet a filter level pruning method for deep neural network compression"
    - "coordinating filters for faster deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "weight normalization based quantization for deep neural network compression"):
  with members:
    - "regularizing activation distribution for training binarized deep networks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "simultaneously optimizing weight and quantizer of ternary neural network using truncated gaussian approximation"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "synaptic strength for convolutional neural network"
    - "heterogeneous bitwidth binarization in convolutional neural networks"
    - "network sketching exploiting binary structure in deep cnns"
    - "deep mutual learning"

Connected through hyperedge HE-8 (induced by source paper "oicsr out in channel sparsity regularization for compact deep neural networks"):
  with members:
    - "learning to prune filters in convolutional neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "layer compensated pruning for resource constrained convolutional neural networks"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"
    - "data driven sparse structure selection for deep neural networks"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

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)