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: "domain adaptive deep network compression"
  Abstract: Deep Neural Networks trained on large datasets can be easily transferred to new domains with far fewer labeled examples by a process called fine-tuning. This has the advantage that representations learned in the large source domain can be exploited on smaller target domains. However, networks designed to be optimal for the source task are often prohibitively large for the target task. In this work we address the compression of networks after domain transfer. #R##N#We focus on compression algorithms based on low-rank matrix decomposition. Existing methods base compression solely on learned network weights and ignore the statistics of network activations. We show that domain transfer leads to large shifts in network activations and that it is desirable to take this into account when compressing. We demonstrate that considering activation statistics when compressing weights leads to a rank-constrained regression problem with a closed-form solution. Because our method takes into account the target domain, it can more optimally remove the redundancy in the weights. Experiments show that our Domain Adaptive Low Rank (DALR) method significantly outperforms existing low-rank compression techniques. With our approach, the fc6 layer of VGG19 can be compressed more than 4x more than using truncated SVD alone -- with only a minor or no loss in accuracy. When applied to domain-transferred networks it allows for compression down to only 5-20% of the original number of parameters with only a minor drop in performance.

Connected through hyperedge HE-1 (induced by source paper "adaptative inference cost with convolutional neural mixture models"):
  with members:
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "amc automl for model compression and acceleration on mobile devices"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "maskconnect connectivity learning by gradient descent"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-2 (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"
    - "extreme network compression via filter group approximation"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"
    - "thinet a filter level pruning method for deep neural network compression"
    - "coordinating filters for faster deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "full deep neural network training on a pruned weight budget"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "revisiting small batch training for deep neural networks"
    - "learning efficient convolutional networks through network slimming"
    - "cnvlutin2 ineffectual activation and weight free deep neural network computing"
    - "training quantized nets a deeper understanding"
    - "thinet a filter level pruning method for deep neural network compression"
    - "deep learning with low precision by half wave gaussian quantization"
    - "incremental network quantization towards lossless cnns with low precision weights"

Connected through hyperedge HE-4 (induced by source paper "domain adaptation regularization for spectral pruning"):
  with members:
    - "compression based bound for non compressed network unified generalization error analysis of large compressible deep neural network"
    - "accelerating deep unsupervised domain adaptation with transfer channel pruning"
    - "target aware network adaptation for efficient representation learning"
    - "network compression using correlation analysis of layer responses"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "fine pruning joint fine tuning and compression of a convolutional network with bayesian optimization"
    - "learning to prune deep neural networks via layer wise optimal brain surgeon"
    - "pruning convolutional neural networks for resource efficient inference"

Connected through hyperedge HE-5 (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"
    - "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"
    - "scnn an accelerator for compressed sparse convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "extreme network compression via filter group approximation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"
    - "skip connections eliminate singularities"
    - "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "compression aware training of deep networks"

Connected through hyperedge HE-7 (induced by source paper "efficient structured pruning and architecture searching for group convolution"):
  with members:
    - "fully learnable group convolution for acceleration of deep neural networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "building efficient deep neural networks with unitary group convolutions"
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "extreme network compression via filter group approximation"
    - "mnasnet platform aware neural architecture search for mobile"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-8 (induced by source paper "network compression using correlation analysis of layer responses"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "dnn or k nn that is the generalize vs memorize question"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "compressing neural networks using the variational information bottleneck"
    - "nisp pruning networks using neuron importance score propagation"
    - "thinet a filter level pruning method for deep neural network compression"
    - "compression aware training of deep 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)