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: "a fast and compact saliency score regression network based on fully convolutional network"
  Abstract: Visual saliency detection aims at identifying the most visually distinctive parts in an image, and serves as a pre-processing step for a variety of computer vision and image processing tasks. To this end, the saliency detection procedure must be as fast and compact as possible and optimally processes input images in a real time manner. However, contemporary detection methods always take hundreds of milliseconds to pursue feeble improvements on the detection precession. In this paper, we tackle this problem by proposing a fast and compact salient score regression network which employs deep convolutional neural networks (CNN) to estimate the saliency of objects in images. It operates (including training and testing) in an end-to-end manner (image-to-image prediction) and also directly produces whole saliency maps from original images without any pre-processings and post-processings. Comparing with contemporary CNN-based saliency detection methods, the proposed method extremely simplifies the detection procedure and further promotes the representation ability of CNN for the saliency detection. Our method is evaluated on six public datasets, and experimental results show that the precision can be comparable to the published state-of-the-art methods while the speed gets a significant improvement (35 FPS, processing in real time).

Connected through hyperedge HE-1 (induced by source paper "masking salient object detection a mask region based convolutional neural network analysis for segmentation of salient objects"):
  with members:
    - "salient object detection with lossless feature reflection and weighted structural loss"
    - "a simple pooling based design for real time salient object detection"
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "salient object detection by lossless feature reflection"
    - "robust iris segmentation based on fully convolutional networks and generative adversarial networks"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "weakly supervised salient object detection using image labels"

Connected through hyperedge HE-2 (induced by source paper "anda a novel data augmentation technique applied to salient object detection"):
  with members:
    - "an efficient and layout independent automatic license plate recognition system based on the yolo detector"
    - "a simple pooling based design for real time salient object detection"
    - "masking salient object detection a mask region based convolutional neural network analysis for segmentation of salient objects"
    - "salient object detection a survey"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "memory efficient deep salient object segmentation networks on gridized superpixels"
    - "deep saliency with encoded low level distance map and high level features"
    - "visual saliency based on multiscale deep features"

Connected through hyperedge HE-3 (induced by source paper "image based localization using hourglass networks"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "demon depth and motion network for learning monocular stereo"
    - "stacked hourglass networks for human pose estimation"
    - "deep image retrieval learning global representations for image search"
    - "learning to refine object segments"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "modelling uncertainty in deep learning for camera relocalization"

Connected through hyperedge HE-4 (induced by source paper "training deep networks to be spatially sensitive"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deep contrast learning for salient object detection"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "deep dense and low rank gaussian conditional random fields"
    - "visual saliency detection based on multiscale deep cnn features"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "feedforward semantic segmentation with zoom out features"

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)