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 benchmark dataset and saliency guided stacked autoencoders for video based salient object detection"
  Abstract: Image-based salient object detection (SOD) has been extensively studied in the past decades. However, video-based SOD is much less explored since there lack large-scale video datasets within which salient objects are unambiguously defined and annotated. Toward this end, this paper proposes a video-based SOD dataset that consists of 200 videos (64 minutes). In constructing the dataset, we manually annotate all objects and regions over 7,650 uniformly sampled keyframes and collect the eye-tracking data of 23 subjects that free-view all videos. From the user data, we find salient objects in video can be defined as objects that consistently pop-out throughout the video, and objects with such attributes can be unambiguously annotated by combining manually annotated object/region masks with eye-tracking data of multiple subjects. To the best of our knowledge, it is currently the largest dataset for video-based salient object detection. #R##N#Based on this dataset, this paper proposes an unsupervised baseline approach for video-based SOD by using saliency-guided stacked autoencoders. In the proposed approach, multiple spatiotemporal saliency cues are first extracted at pixel, superpixel and object levels. With these saliency cues, stacked autoencoders are unsupervisedly constructed which automatically infer a saliency score for each pixel by progressively encoding the high-dimensional saliency cues gathered from the pixel and its spatiotemporal neighbors. Experimental results show that the proposed unsupervised approach outperforms 30 state-of-the-art models on the proposed dataset, including 19 image-based & classic (unsupervised or non-deep learning), 6 image-based & deep learning, and 5 video-based & unsupervised. Moreover, benchmarking results show that the proposed dataset is very challenging and has the potential to boost the development of video-based SOD.

Connected through hyperedge HE-1 (induced by source paper "human vs machine attention in neural networks a comparative study"):
  with members:
    - "interaction aware spatio temporal pyramid attention networks for action classification"
    - "reciprocal attention fusion for visual question answering"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "adding attentiveness to the neurons in recurrent neural networks"
    - "fine grained video categorization with redundancy reduction attention"
    - "reverse attention for salient object detection"
    - "learn to pay attention"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-2 (induced by source paper "distortion adaptive salient object detection in 360 circ omnidirectional images"):
  with members:
    - "predicting head movement in panoramic video a deep reinforcement learning approach"
    - "deeply supervised salient object detection with short connections"
    - "cube padding for weakly supervised saliency prediction in 360 deg videos"
    - "reverse attention for salient object detection"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "picanet learning pixel wise contextual attention for saliency detection"
    - "amulet aggregating multi level convolutional features for salient object detection"

Connected through hyperedge HE-3 (induced by source paper "three birds one stone a general architecture for salient object segmentation edge detection and skeleton extraction"):
  with members:
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "visual saliency prediction using a mixture of deep neural networks"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "rgbd salient object detection via deep fusion"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "richer convolutional features for edge detection"
    - "srn side output residual network for object symmetry detection in the wild"

Connected through hyperedge HE-4 (induced by source paper "semantic instance meets salient object study on video semantic salient instance segmentation"):
  with members:
    - "video salient object detection via fully convolutional networks"
    - "reconstruction network for video captioning"
    - "path aggregation network for instance segmentation"
    - "relation networks for object detection"
    - "the 2017 davis challenge on video object segmentation"
    - "instance level salient object segmentation"
    - "detect and track efficient pose estimation in videos"
    - "masklab instance segmentation by refining object detection with semantic and direction features"

Connected through hyperedge HE-5 (induced by source paper "how drones look crowdsourced knowledge transfer for aerial video saliency prediction"):
  with members:
    - "deep visual attention prediction"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "end to end saliency mapping via probability distribution prediction"
    - "video salient object detection via fully convolutional networks"
    - "squeeze and excitation networks"
    - "saliency detection by forward and backward cues in deep cnns"
    - "wepsam weakly pre learnt saliency model"
    - "spatio temporal saliency networks for dynamic saliency prediction"

Connected through hyperedge HE-6 (induced by source paper "review of visual saliency detection with comprehensive information"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "co saliency detection for rgbd images based on multi constraint feature matching and cross label propagation"
    - "video salient object detection via fully convolutional networks"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "rgbd salient object detection via deep fusion"
    - "an iterative co saliency framework for rgbd images"
    - "integrated deep and shallow networks for salient object detection"
    - "group wise deep co saliency detection"

Connected through hyperedge HE-7 (induced by source paper "exploiting the value of the center dark channel prior for salient object detection"):
  with members:
    - "salient object detection a survey"
    - "pdnet prior model guided depth enhanced network for salient object detection"
    - "automatic salient object detection for panoramic images using region growing and fixation prediction model"
    - "instance level salient object segmentation"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "inner and inter label propagation salient object detection in the wild"
    - "visual saliency based on multiscale deep features"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-8 (induced by source paper "hierarchical deep co segmentation of primary objects in aerial videos"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "deep contrast learning for salient object detection"
    - "salient object detection a benchmark"
    - "microsoft coco common objects in context"

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)