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: "deeprebirth accelerating deep neural network execution on mobile devices"
  Abstract: Deploying deep neural networks on mobile devices is a challenging task. Current model compression methods such as matrix decomposition effectively reduce the deployed model size, but still cannot satisfy real-time processing requirement. This paper first discovers that the major obstacle is the excessive execution time of non-tensor layers such as pooling and normalization without tensor-like trainable parameters. This motivates us to design a novel acceleration framework: DeepRebirth through "slimming" existing consecutive and parallel non-tensor and tensor layers. The layer slimming is executed at different substructures: (a) streamline slimming by merging the consecutive non-tensor and tensor layer vertically; (b) branch slimming by merging non-tensor and tensor branches horizontally. The proposed optimization operations significantly accelerate the model execution and also greatly reduce the run-time memory cost since the slimmed model architecture contains less hidden layers. To maximally avoid accuracy loss, the parameters in new generated layers are learned with layer-wise fine-tuning based on both theoretical analysis and empirical verification. As observed in the experiment, DeepRebirth achieves more than 3x speed-up and 2.5x run-time memory saving on GoogLeNet with only 0.4% drop of top-5 accuracy on ImageNet. Furthermore, by combining with other model compression techniques, DeepRebirth offers an average of 65ms inference time on the CPU of Samsung Galaxy S6 with 86.5% top-5 accuracy, 14% faster than SqueezeNet which only has a top-5 accuracy of 80.5%.

Connected through hyperedge HE-1 (induced by source paper "flame a self adaptive auto labeling system for heterogeneous mobile processors"):
  with members:
    - "cross modal data programming enables rapid medical machine learning"
    - "federated learning strategies for improving communication efficiency"
    - "runtime concurrency control and operation scheduling for high performance neural network training"
    - "deep co training for semi supervised image recognition"
    - "inferring generative model structure with static analysis"
    - "snorkel rapid training data creation with weak supervision"
    - "morphnet fast simple resource constrained structure learning of deep networks"
    - "learning the structure of generative models without labeled data"

Connected through hyperedge HE-2 (induced by source paper "previous a methodology for prediction of visual inference performance on iot devices"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "design automation for efficient deep learning computing"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "effnet an efficient structure for convolutional neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "benchmark analysis of representative deep neural network architectures"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"

Connected through hyperedge HE-3 (induced by source paper "codex bit flexible encoding for streaming based fpga acceleration of dnns"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "a hardware friendly algorithm for scalable training and deployment of dimensionality reduction models on fpga"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "wrpn wide reduced precision networks"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep learning with low precision by half wave gaussian quantization"
    - "rebnet residual binarized neural network"

Connected through hyperedge HE-4 (induced by source paper "micik mining cross layer inherent similarity knowledge for deep model compression"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "dynamically hierarchy revolution dirnet for compressing recurrent neural network on mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "exploring the regularity of sparse structure in convolutional neural networks"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"
    - "channel pruning for accelerating very deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-5 (induced by source paper "herald optimizing heterogeneous dnn accelerators for edge devices"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "ai benchmark running deep neural networks on android smartphones"
    - "understanding reuse performance and hardware cost of dnn dataflows a data centric approach"
    - "learning depth from single images with deep neural network embedding focal length"
    - "in datacenter performance analysis of a tensor processing unit"
    - "end to end global to local cnn learning for hand pose recovery in depth data"
    - "aggregated residual transformations for deep neural networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "quantized densely connected u nets for efficient landmark localization"):
  with members:
    - "learning to forecast and refine residual motion for image to video generation"
    - "red net a recurrent encoder decoder network for video based face alignment"
    - "training and inference with integers in deep neural networks"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "learning feature pyramids for human pose estimation"
    - "memory efficient implementation of densenets"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"

Connected through hyperedge HE-7 (induced by source paper "lcanet end to end lipreading with cascaded attention ctc"):
  with members:
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "draps dynamic and resource aware placement scheme for docker containers in a heterogeneous cluster"
    - "attention is all you need"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "end to end visual speech recognition with lstms"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "reconstruction for feature disentanglement in pose invariant face recognition"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-8 (induced by source paper "rehar robust and efficient human activity recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras"
    - "tube convolutional neural network t cnn for action detection in videos"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "convolutional two stream network fusion for video action recognition"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "flownet learning optical flow with convolutional 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)