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: "salient object detection with lossless feature reflection and weighted structural loss"
  Abstract: Salient object detection, which aims to identify and locate the most salient pixels or regions in images, has been attracting more and more interest due to its various real-world applications. However, this vision task is quite challenging, especially under complex image scenes. Inspired by the intrinsic reflection of natural images, in this paper, we propose a novel feature learning framework for large-scale salient object detection. Specifically, we design a symmetrical fully convolutional network to effectively learn complementary saliency features under the guidance of lossless feature reflection. The location information, together with contextual and semantic information, of salient objects is jointly utilized to supervise the proposed network for more accurate saliency predictions. In addition, to overcome the blurry boundary problem, we propose a new weighted structural loss function to ensure clear object boundaries and spatially consistent saliency. The coarse prediction results are effectively refined by these structural information for performance improvements. The extensive experiments on seven saliency detection datasets demonstrate that our approach achieves consistently superior performance and outperforms the very recent state-of-the-art methods with a large margin.

Connected through hyperedge HE-1 (induced by source paper "rethinking rgb d salient object detection models datasets and large scale benchmarks"):
  with members:
    - "super diffusion for salient object detection"
    - "adaptive fusion for rgb d salient object detection"
    - "salient object detection in the deep learning era an in depth survey"
    - "multi source weak supervision for saliency detection"
    - "saliency integration an arbitrator model"
    - "richer and deeper supervision network for salient object detection"
    - "deeply supervised salient object detection with short connections"
    - "deep reasoning with multi scale context for salient object detection"

Connected through hyperedge HE-2 (induced by source paper "sac net spatial attenuation context for salient object detection"):
  with members:
    - "pyramid feature attention network for saliency detection"
    - "a simple pooling based design for real time salient object detection"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "deeply supervised salient object detection with short connections"
    - "group normalization"
    - "reverse attention for salient object detection"
    - "direction aware spatial context features for shadow detection and removal"
    - "learning uncertain convolutional features for accurate saliency detection"

Connected through hyperedge HE-3 (induced by source paper "egnet edge guidance network for salient object detection"):
  with members:
    - "rethinking rgb d salient object detection models datasets and large scale benchmarks"
    - "richer and deeper supervision network for salient object detection"
    - "ranet ranking attention network for fast video object segmentation"
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "deep reasoning with multi scale context for salient object detection"
    - "boundary guided feature aggregation network for salient object detection"
    - "salient objects in clutter bringing salient object detection to the foreground"

Connected through hyperedge HE-4 (induced by source paper "towards high resolution salient object detection"):
  with members:
    - "auto retoucher art a framework for background replacement and image editing"
    - "deeply supervised salient object detection with short connections"
    - "agile amulet real time salient object detection with contextual attention"
    - "non rigid object tracking via deep multi scale spatial temporal discriminative saliency maps"
    - "fast end to end trainable guided filter"
    - "reverse attention for salient object detection"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "structure measure a new way to evaluate foreground maps"

Connected through hyperedge HE-5 (induced by source paper "masking salient object detection a mask region based convolutional neural network analysis for segmentation of salient objects"):
  with members:
    - "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"
    - "a fast and compact saliency score regression network based on fully convolutional network"

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)