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: "deepvs a deep learning based video saliency prediction approach"
  Abstract: In this paper, we propose a novel deep learning based video saliency prediction method, named DeepVS. Specifically, we establish a large-scale eye-tracking database of videos (LEDOV), which includes 32 subjects’ fixations on 538 videos. We find from LEDOV that human attention is more likely to be attracted by objects, particularly the moving objects or the moving parts of objects. Hence, an object-to-motion convolutional neural network (OM-CNN) is developed to predict the intra-frame saliency for DeepVS, which is composed of the objectness and motion subnets. In OM-CNN, cross-net mask and hierarchical feature normalization are proposed to combine the spatial features of the objectness subnet and the temporal features of the motion subnet. We further find from our database that there exists a temporal correlation of human attention with a smooth saliency transition across video frames. We thus propose saliency-structured convolutional long short-term memory (SS-ConvLSTM) network, using the extracted features from OM-CNN as the input. Consequently, the inter-frame saliency maps of a video can be generated, which consider both structured output with center-bias and cross-frame transitions of human attention maps. Finally, the experimental results show that DeepVS advances the state-of-the-art in video saliency prediction.

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"
    - "audio visual event localization in unconstrained videos"
    - "end to end saliency mapping via probability distribution prediction"
    - "susinet see understand and summarize it"
    - "the sound of pixels"

Connected through hyperedge HE-2 (induced by source paper "video saliency prediction using enhanced spatiotemporal alignment network"):
  with members:
    - "reppoints point set representation for object detection"
    - "simple vs complex temporal recurrences for video saliency prediction"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "deep visual attention prediction"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "tdan temporally deformable alignment network for video super resolution"
    - "deformable convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "unified image and video saliency modeling"):
  with members:
    - "domain specific batch normalization for unsupervised domain adaptation"
    - "tased net temporally aggregating spatial encoder decoder network for video saliency detection"
    - "deep visual attention prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "saliency prediction in the deep learning era an empirical investigation"
    - "end to end saliency mapping via probability distribution prediction"

Connected through hyperedge HE-4 (induced by source paper "exploiting temporal consistency for real time video depth estimation"):
  with members:
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "rethinking monocular depth estimation with adversarial training"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "ganvo unsupervised deep monocular visual odometry and depth estimation with generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "tased net temporally aggregating spatial encoder decoder network for video saliency detection"):
  with members:
    - "deep visual attention prediction"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "the kinetics human action video dataset"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "spatio temporal saliency networks for dynamic saliency prediction"

Connected through hyperedge HE-6 (induced by source paper "dada a large scale benchmark and model for driver attention prediction in accidental scenarios"):
  with members:
    - "lates latent space distillation for teacher student driving policy learning"
    - "periphery fovea multi resolution driving model guided by human attention"
    - "salient object detection in the deep learning era an in depth survey"
    - "unsupervised traffic accident detection in first person videos"
    - "saliency prediction in the deep learning era an empirical investigation"
    - "yolov3 an incremental improvement"
    - "joint attention in driver pedestrian interaction from theory to practice"
    - "latent space autoregression for novelty detection"

Connected through hyperedge HE-7 (induced by source paper "motion aware feature for improved video anomaly detection"):
  with members:
    - "graphical contrastive losses for scene graph parsing"
    - "real world anomaly detection in surveillance videos"
    - "towards universal representation for unseen action recognition"
    - "large scale visual relationship understanding"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "tube convolutional neural network t cnn for action detection in videos"

Connected through hyperedge HE-8 (induced by source paper "ultrafast video attention prediction with coupled knowledge distillation"):
  with members:
    - "deep visual attention prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "how drones look crowdsourced knowledge transfer for aerial video saliency prediction"
    - "cbam convolutional block attention module"
    - "deep mutual learning"
    - "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features"
    - "spatio temporal saliency networks for dynamic saliency prediction"
    - "deepgaze ii reading fixations from deep features trained on object recognition"

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)