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 objects in clutter bringing salient object detection to the foreground"
  Abstract: In this paper, we provide a comprehensive evaluation of salient object detection (SOD) models. Our analysis identifies a serious design bias of existing SOD datasets which assumes that each image contains at least one clearly outstanding salient object in low clutter. This is an unrealistic assumption. The design bias has led to a saturated high performance for state of the art SOD models when evaluated on existing datasets. The models, however, still perform far from being satisfactory when applied to real-world daily scenes. Based on our analyses, we first identify 7 crucial aspects that a comprehensive and balanced dataset should fulfill. Then, we propose a new high quality dataset and update the previous saliency benchmark. Specifically, our dataset called \textbf{SOC}, Salient Objects in Clutter, includes images with salient and non-salient objects from daily object categories. Beyond object category annotations, each salient image is accompanied by attributes (\emph{e.g., appearance change, clutter}) that reflect common challenges in real-world scenes, and can help 1) gain a deeper insight into the SOD problem, 2) investigate the pros and cons of the SOD models, and 3) objectively assess models from different perspectives. Finally, we report attribute-based performance assessment on our SOC dataset. We believe that our dataset and results will open new directions for future research on salient object detection.

Connected through hyperedge HE-1 (induced by source paper "motion attentive transition for zero shot video object segmentation"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "zero shot video object segmentation via attentive graph neural networks"
    - "rvos end to end recurrent network for video object segmentation"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "video object segmentation using teacher student adaptation in a human robot interaction hri setting"
    - "cbam convolutional block attention module"
    - "learning to segment moving objects"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

Connected through hyperedge HE-2 (induced by source paper "efficient and accurate arbitrary shaped text detection with pixel aggregation network"):
  with members:
    - "rethinking rgb d salient object detection models datasets and large scale benchmarks"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "region refinement network for salient object detection"):
  with members:
    - "se2net siamese edge enhancement network for salient object detection"
    - "a simple pooling based design for real time salient object detection"
    - "dna deeply supervised nonlinear aggregation for salient object detection"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "selectivity or invariance boundary aware salient object detection"
    - "revisiting salient object detection simultaneous detection ranking and subitizing of multiple salient objects"

Connected through hyperedge HE-4 (induced by source paper "joint learning of saliency detection and weakly supervised semantic segmentation"):
  with members:
    - "multi source weak supervision for saliency detection"
    - "semantic correlation promoted shape variant context for segmentation"
    - "boundary aware feature propagation for scene segmentation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "reverse attention for salient object detection"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "self explanatory deep salient object detection"

Connected through hyperedge HE-5 (induced by source paper "where is my mirror"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "path aggregation network for instance segmentation"
    - "reverse attention for salient object detection"
    - "cbam convolutional block attention module"
    - "context encoding for semantic segmentation"
    - "a d net training a shadow detector with adversarial shadow attenuation"
    - "direction aware spatial context features for shadow detection"
    - "picanet learning pixel wise contextual attention for saliency detection"

Connected through hyperedge HE-6 (induced by source paper "cagnet content aware guidance for salient object detection"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "connnet a long range relation aware pixel connectivity network for salient segmentation"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "deeply supervised salient object detection with short connections"
    - "saliency for fine grained object recognition in domains with scarce training data"
    - "learning a discriminative feature network for semantic segmentation"
    - "probabilistic saliency estimation"
    - "video salient object detection via fully convolutional networks"

Connected through hyperedge HE-7 (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"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "structured modeling of joint deep feature and prediction refinement for salient object detection"):
  with members:
    - "bayesian loss for crowd count estimation with point supervision"
    - "saliency integration an arbitrator model"
    - "deeply supervised salient object detection with short connections"
    - "monocular depth estimation using multi scale continuous crfs as sequential deep networks"
    - "reverse attention for salient object detection"
    - "learning deep structured multi scale features using attention gated crfs for contour prediction"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "picanet learning pixel wise contextual attention for saliency detection"

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)