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: "rethinking rgb d salient object detection models datasets and large scale benchmarks"
  Abstract: The use of RGB-D information for salient object detection has been explored in recent years. However, relatively few efforts have been spent in modeling salient object detection over real-world human activity scenes with RGB-D. In this work, we fill the gap by making the following contributions to RGB-D salient object detection. First, we carefully collect a new salient person (SIP) dataset, which consists of 1K high-resolution images that cover diverse real-world scenes from various viewpoints, poses, occlusion, illumination, and background. Second, we conduct a large-scale and so far the most comprehensive benchmark comparing contemporary methods, which has long been missing in the area and can serve as a baseline for future research. We systematically summarized 31 popular models, evaluated 17 state-of-the-art methods over seven datasets with totally about 91K images. Third, we propose a simple baseline architecture, called Deep Depth-Depurator Network (D3Net). It consists of a depth depurator unit and a feature learning module, performing initial low-quality depth map filtering and cross-modal feature learning respectively. These components form a nested structure and are elaborately designed to be learned jointly. D3Net exceeds the performance of any prior contenders across five metrics considered, thus serves as a strong baseline to advance the research frontier. We also demonstrate that D3Net can be used to efficiently extract salient person masks from the real scenes, enabling effective background changed book cover application with 20 fps on a single GPU. All the saliency maps, our new SIP dataset, baseline model, and evaluation tools are made publicly available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "depth potentiality aware gated attention network for rgb d salient object detection"):
  with members:
    - "adaptive fusion for rgb d salient object detection"
    - "a simple pooling based design for real time salient object detection"
    - "egnet edge guidance network for salient object detection"
    - "pdnet prior model guided depth enhanced network for salient object detection"
    - "rgbd salient object detection via deep fusion"
    - "non local neural networks"
    - "attention is all you need"
    - "structure measure a new way to evaluate foreground maps"

Connected through hyperedge HE-2 (induced by source paper "a fixation based 360 benchmark dataset for salient object detection"):
  with members:
    - "global context aware progressive aggregation network for salient object detection"
    - "distortion adaptive salient object detection in 360 circ omnidirectional images"
    - "predicting head movement in panoramic video a deep reinforcement learning approach"
    - "a simple pooling based design for real time salient object detection"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "egnet edge guidance network for salient object detection"
    - "enhanced alignment measure for binary foreground map evaluation"
    - "bridge the gap between vqa and human behavior on omnidirectional video a large scale dataset and a deep learning model"

Connected through hyperedge HE-3 (induced by source paper "efficient and accurate arbitrary shaped text detection with pixel aggregation network"):
  with members:
    - "selective kernel networks"
    - "shape robust text detection with progressive scale expansion network"
    - "rotation sensitive regression for oriented scene text detection"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "cu net coupled u nets"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "salient objects in clutter bringing salient object detection to the foreground"

Connected through hyperedge HE-4 (induced by source paper "ranet ranking attention network for fast video object segmentation"):
  with members:
    - "zero shot video object segmentation via attentive graph neural networks"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "egnet edge guidance network for salient object detection"
    - "fast and accurate online video object segmentation via tracking parts"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "videomatch matching based video object segmentation"
    - "stereonet guided hierarchical refinement for real time edge aware depth prediction"

Connected through hyperedge HE-5 (induced by source paper "egnet edge guidance network for salient object detection"):
  with members:
    - "salient object detection with lossless feature reflection and weighted structural loss"
    - "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-6 (induced by source paper "scoot a perceptual metric for facial sketches"):
  with members:
    - "nima neural image assessment"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "enhanced alignment measure for binary foreground map evaluation"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "a multilayer based framework for online background subtraction with freely moving cameras"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "random sampling for fast face sketch synthesis"
    - "structure measure a new way to evaluate foreground maps"

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)