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: "learning gaze transitions from depth to improve video saliency estimation"
  Abstract: In this paper we introduce a novel Depth-Aware Video Saliency approach to predict human focus of attention when viewing RGBD videos on regular 2D screens. We train a generative convolutional neural network which predicts a saliency map for a frame, given the fixation map of the previous frame. Saliency estimation in this scenario is highly important since in the near future 3D video content will be easily acquired and yet hard to display. This can be explained, on the one hand, by the dramatic improvement of 3D-capable acquisition equipment. On the other hand, despite the considerable progress in 3D display technologies, most of the 3D displays are still expensive and require wearing special glasses. To evaluate the performance of our approach, we present a new comprehensive database of eye-fixation ground-truth for RGBD videos. Our experiments indicate that integrating depth into video saliency calculation is beneficial. We demonstrate that our approach outperforms state-of-the-art methods for video saliency, achieving 15% relative improvement.

Connected through hyperedge HE-1 (induced by source paper "stavis spatio temporal audiovisual saliency network"):
  with members:
    - "dave a deep audio visual embedding for dynamic saliency prediction"
    - "the sound of motions"
    - "learning to localize sound source in visual scenes"
    - "deep visual attention prediction"
    - "deepvs a deep learning based video saliency prediction approach"
    - "audio visual event localization in unconstrained videos"
    - "end to end saliency mapping via probability distribution prediction"
    - "susinet see understand and summarize it"

Connected through hyperedge HE-2 (induced by source paper "believe it or not we know what you are looking at"):
  with members:
    - "salgan visual saliency prediction with generative adversarial networks"
    - "eye tracking for everyone"
    - "feature pyramid networks for object detection"
    - "deep residual learning for image recognition"
    - "recurrent network models for human dynamics"
    - "appearance based gaze estimation in the wild"
    - "flowing convnets for human pose estimation in videos"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "joint attention in driver pedestrian interaction from theory to practice"):
  with members:
    - "when vehicles see pedestrians with phones a multi cue framework for recognizing phone based activities of pedestrians"
    - "video salient object detection via fully convolutional networks"
    - "agreeing to cross how drivers and pedestrians communicate"
    - "citypersons a diverse dataset for pedestrian detection"
    - "integrating three mechanisms of visual attention for active visual search"
    - "learning where to attend like a human driver"
    - "real time human motion capture with multiple depth cameras"
    - "hcp a flexible cnn framework for multi label image classification"

Connected through hyperedge HE-4 (induced by source paper "saliency prediction in the deep learning era successes limitations and future challenges"):
  with members:
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "deep visual attention prediction"
    - "charades ego a large scale dataset of paired third and first person videos"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "learning a saliency evaluation metric using crowdsourced perceptual judgments"
    - "deepvs a deep learning based video saliency prediction approach"

Connected through hyperedge HE-5 (induced by source paper "susinet see understand and summarize it"):
  with members:
    - "deep visual attention prediction"
    - "deepvs a deep learning based video saliency prediction approach"
    - "taskonomy disentangling task transfer learning"
    - "video summarization using fully convolutional sequence networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "multimodal visual concept learning with weakly supervised techniques"

Connected through hyperedge HE-6 (induced by source paper "global and local sensitivity guided key salient object re augmentation for video saliency detection"):
  with members:
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "deep visual attention prediction"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "deepvs a deep learning based video saliency prediction approach"
    - "video salient object detection via fully convolutional networks"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "exploiting saliency for object segmentation from image level labels"
    - "supervising neural attention models for video captioning by human gaze data"

Connected through hyperedge HE-7 (induced by source paper "how do people explore virtual environments"):
  with members:
    - "movie editing and cognitive event segmentation in virtual reality video"
    - "making 360 circ video watchable in 2d learning videography for click free viewing"
    - "a deep multi level network for saliency prediction"
    - "shallow and deep convolutional networks for saliency prediction"
    - "what do different evaluation metrics tell us about saliency models"
    - "deep learning for saliency prediction in natural video"
    - "visual saliency based on multiscale deep features"
    - "fixation prediction with a combined model of bottom up saliency and vanishing point"

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)