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: "factorized convolutional neural networks"
  Abstract: Deep convolutional neural networks achieve better than human level visual recognition accuracy, at the cost of high computational complexity. We propose to factorize the convolutional layers to improve their efficiency. In traditional convolutional layers, the 3D convolution can be considered as performing in-channel spatial convolution and linear channel projection simultaneously, leading to highly redundant computation. By unravelling them apart, the proposed layer only involves single in-channel convolution and linear channel projection. When stacking such layers together, we achieves similar accuracy with significantly less computation. Additionally, we propose a topological connection framework between the input channels and output channels that further improves the layer's efficiency. Our experiments demonstrate that the proposed method remarkably outperforms the standard convolutional layer with regard to accuracy/complexity ratio. Our model achieves accuracy of GoogLeNet while consuming 3.4 times less computation.

Connected through hyperedge HE-1 (induced by source paper "depthwise non local module for fast salient object detection using a single thread"):
  with members:
    - "motion guided attention for video salient object detection"
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "a simple pooling based design for real time salient object detection"
    - "non local context encoder robust biomedical image segmentation against adversarial attacks"
    - "rosa robust salient object detection against adversarial attacks"
    - "deeply supervised salient object detection with short connections"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "contrast oriented deep neural networks for salient object detection"

Connected through hyperedge HE-2 (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"
    - "extreme network compression via filter group approximation"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-3 (induced by source paper "hbonet harmonious bottleneck on two orthogonal dimensions"):
  with members:
    - "on the convergence of adam and beyond"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "accelerating deep neural networks with spatial bottleneck modules"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "efficient neural architecture search via parameter sharing"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "in defense of pre trained imagenet architectures for real time semantic segmentation of road driving images"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "guided upsampling network for real time semantic segmentation"
    - "light weight refinenet for real time semantic segmentation"
    - "efficient semantic segmentation using gradual grouping"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning transferable architectures for scalable image recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "linknet exploiting encoder representations for efficient semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "openei an open framework for edge intelligence"):
  with members:
    - "pcamp performance comparison of machine learning packages on the edges"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "cavbench a benchmark suite for connected and autonomous vehicles"
    - "yolov3 an incremental improvement"
    - "in datacenter performance analysis of a tensor processing unit"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "distributed deep neural networks over the cloud the edge and end devices"

Connected through hyperedge HE-6 (induced by source paper "residual pyramid learning for single shot semantic segmentation"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "light weight refinenet for real time semantic segmentation"
    - "icnet for real time semantic segmentation on high resolution images"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "fast and accurate image super resolution with deep laplacian pyramid networks"
    - "rethinking atrous convolution for semantic image segmentation"
    - "impatient dnns deep neural networks with dynamic time budgets"

Connected through hyperedge HE-7 (induced by source paper "zygarde time sensitive on device deep intelligence on intermittently powered systems"):
  with members:
    - "alpaca intermittent execution without checkpoints"
    - "rethinking floating point for deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "fastdeepiot towards understanding and optimizing neural network execution time on mobile and embedded devices"
    - "adaptive neural networks for efficient inference"
    - "deepiot compressing deep neural network structures for sensing systems with a compressor critic framework"
    - "rdeepsense reliable deep mobile computing models with uncertainty estimations"
    - "projectionnet learning efficient on device deep networks using neural projections"

Connected through hyperedge HE-8 (induced by source paper "towards real time automatic portrait matting on mobile devices"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "semantic human matting"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "fast deep matting for portrait animation on mobile phone"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep image matting"
    - "enet a deep neural network architecture for real time semantic segmentation"

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)