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: "efficient neural network compression"
  Abstract: Network compression reduces the computational complexity and memory consumption of deep neural networks by reducing the number of parameters. In SVD-based network compression, the right rank needs to be decided for every layer of the network. In this paper, we propose an efficient method for obtaining the rank configuration of the whole network. Unlike previous methods which consider each layer separately, our method considers the whole network to choose the right rank configuration. We propose novel accuracy metrics to represent the accuracy and complexity relationship for a given neural network. We use these metrics in a non-iterative fashion to obtain the right rank configuration which satisfies the constraints on FLOPs and memory while maintaining sufficient accuracy. Experiments show that our method provides better compromise between accuracy and computational complexity/memory consumption while performing compression at much higher speed. For VGG-16 our network can reduce the FLOPs by 25% and improve accuracy by 0.7% compared to the baseline, while requiring only 3 minutes on a CPU to search for the right rank configuration. Previously, similar results were achieved in 4 hours with 8 GPUs. The proposed method can be used for lossless compression of a neural network as well. The better accuracy and complexity compromise, as well as the extremely fast speed of our method makes it suitable for neural network compression.

Connected through hyperedge HE-1 (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-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"
    - "private model compression via knowledge distillation"

Connected through hyperedge HE-3 (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"
    - "multi bin trainable linear unit for fast image restoration networks"
    - "exploiting kernel sparsity and entropy for interpretable cnn compression"
    - "extreme network compression via filter group approximation"
    - "learning efficient convolutional networks through network slimming"
    - "enhanced deep residual networks for single image super resolution"

Connected through hyperedge HE-4 (induced by source paper "localization aware channel pruning for object detection"):
  with members:
    - "centernet keypoint triplets for object detection"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "focused quantization for sparse cnns"
    - "mask scoring r cnn"
    - "objects as points"
    - "play and prune adaptive filter pruning for deep model compression"
    - "cornernet detecting objects as paired keypoints"
    - "discrimination aware channel pruning for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "taxonomy and evaluation of structured compression of convolutional neural networks"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "metapruning meta learning for automatic neural network channel pruning"
    - "eigendamage structured pruning in the kronecker factored eigenbasis"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "the state of sparsity in deep neural networks"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-6 (induced by source paper "darb a density aware regular block pruning for deep neural networks"):
  with members:
    - "compressing rnns for iot devices by 15 38x using kronecker products"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "rethinking full connectivity in recurrent neural networks"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "rethinking the value of network pruning"
    - "attention is all you need"

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)