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: "what is a salient object a dataset and a baseline model for salient object detection"
  Abstract: Salient object detection or salient region detection models, diverging from fixation prediction models, have traditionally been dealing with locating and segmenting the most salient object or region in a scene. While the notion of most salient object is sensible when multiple objects exist in a scene, current datasets for evaluation of saliency detection approaches often have scenes with only one single object. We introduce three main contributions in this paper. First, we take an in-depth look at the problem of salient object detection by studying the relationship between where people look in scenes and what they choose as the most salient object when they are explicitly asked. Based on the agreement between fixations and saliency judgments, we then suggest that the most salient object is the one that attracts the highest fraction of fixations. Second, we provide two new less biased benchmark data sets containing scenes with multiple objects that challenge existing saliency models. Indeed, we observed a severe drop in performance of eight state-of-the-art models on our data sets (40%–70%). Third, we propose a very simple yet powerful model based on superpixels to be used as a baseline for model evaluation and comparison. While on par with the best models on MSRA-5 K data set, our model wins over other models on our data highlighting a serious drawback of existing models, which is convoluting the processes of locating the most salient object and its segmentation. We also provide a review and statistical analysis of some labeled scene data sets that can be used for evaluating salient object detection models. We believe that our work can greatly help remedy the over-fitting of models to existing biased data sets and opens new venues for future research in this fast-evolving field.

Connected through hyperedge HE-1 (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"
    - "rethinking rgb d salient object detection models datasets and large scale benchmarks"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "salient object detection a survey"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "instance level salient object segmentation"
    - "disc deep image saliency computing via progressive representation learning"
    - "deep saliency with encoded low level distance map and high level features"
    - "deep contrast learning for salient object detection"
    - "saliency detection via combining region level and pixel level predictions with cnns"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "what makes for effective detection proposals"

Connected through hyperedge HE-3 (induced by source paper "salient object detection with lossless feature reflection and weighted structural loss"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "agile amulet real time salient object detection with contextual attention"
    - "boundary guided feature aggregation network for salient object detection"
    - "interactive deep colorization with simultaneous global and local inputs"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "structure measure a new way to evaluate foreground maps"
    - "two stream flow guided convolutional attention networks for action recognition"
    - "picanet learning pixel wise contextual attention for saliency detection"

Connected through hyperedge HE-4 (induced by source paper "supervised saliency map driven segmentation of lesions in dermoscopic images"):
  with members:
    - "a survey on deep learning in medical image analysis"
    - "salient object detection a discriminative regional feature integration approach"
    - "deep learning ensembles for melanoma recognition in dermoscopy images"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"
    - "an overview of melanoma detection in dermoscopy images using image processing and machine learning"
    - "salient object detection a benchmark"
    - "lesion border detection in dermoscopy images using ensembles of thresholding methods"

Connected through hyperedge HE-5 (induced by source paper "3d quantum cuts for automatic segmentation of porous media in tomography images"):
  with members:
    - "salient object detection a survey"
    - "superpixels an evaluation of the state of the art"
    - "probabilistic saliency estimation"
    - "salient object detection a discriminative regional feature integration approach"
    - "the secrets of salient object segmentation"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"

Connected through hyperedge HE-6 (induced by source paper "analyzing periodicity and saliency for adult video detection"):
  with members:
    - "salient object detection a survey"
    - "a mid level video representation based on binary descriptors a case study for pornography detection"
    - "learning attributes equals multi source domain generalization"
    - "the secrets of salient object segmentation"

Connected through hyperedge HE-7 (induced by source paper "agile amulet real time salient object detection with contextual attention"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "structure measure a new way to evaluate foreground maps"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deep saliency with encoded low level distance map and high level features"
    - "deep contrast learning for salient object detection"

Connected through hyperedge HE-8 (induced by source paper "revisiting video saliency a large scale benchmark and a new model"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "deep visual attention prediction"
    - "deepvs a deep learning based video saliency prediction approach"
    - "end to end saliency mapping via probability distribution prediction"
    - "video salient object detection via fully convolutional networks"
    - "deep cropping via attention box prediction and aesthetics assessment"
    - "residual attention network for image classification"
    - "spatio temporal saliency networks for dynamic saliency prediction"

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)