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: "deep learning for saliency prediction in natural video"
  Abstract: The purpose of this paper is the detection of salient areas in natural video by using the new deep learning techniques. #R##N#Salient patches in video frames are predicted first.#R##N#Then the predicted visual fixation maps are built upon them.  We design the deep architecture on the basis of CaffeNet implemented #R##N#with Caffe toolkit. We show that changing#R##N#the way of data selection for optimisation of network parameters, we can save computation cost up to $12$ times. #R##N#We extend deep learning approaches for saliency prediction in still images with RGB values to specificity of video using the sensitivity #R##N#of the human visual system to residual motion. #R##N#Furthermore, we complete primary colour pixel values by contrast features proposed in classical visual attention prediction models.  #R##N#The experiments are conducted on two publicly available datasets. The first is IRCCYN video database containing $31$ videos with an #R##N#overall amount of $7300$ frames and eye fixations #R##N#of $37$ subjects.#R##N#The second one is HOLLYWOOD2 provided $2517$ movie clips with the eye fixations of $19$ subjects.#R##N#On IRCYYN dataset, the accuracy obtained is of $89.51\% $.#R##N#On HOLLYWOOD2 dataset, results in prediction of saliency of patches show the  improvement up to $2\%$ with regard to RGB use only. #R##N#The resulting accuracy of $76,6\%$ is obtained. #R##N#The AUC metric in comparison of predicted saliency maps with visual fixation maps shows the increase up to $16\%$ on a sample of video #R##N#clips from this dataset.

Connected through hyperedge HE-1 (induced by source paper "deepvs a deep learning based video saliency prediction approach"):
  with members:
    - "revisiting video saliency a large scale benchmark and a new model"
    - "video salient object detection via fully convolutional networks"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "learning where to attend like a human driver"
    - "a deep multi level network for saliency prediction"
    - "spatio temporal saliency networks for dynamic saliency prediction"
    - "shallow and deep convolutional networks for saliency prediction"
    - "deepsaliency multi task deep neural network model for salient object detection"

Connected through hyperedge HE-2 (induced by source paper "supersaliency predicting smooth pursuit based attention with slicing cnns improves fixation prediction for naturalistic videos"):
  with members:
    - "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"
    - "video saliency detection by 3d convolutional neural networks"
    - "spatio temporal saliency networks for dynamic saliency prediction"
    - "what do different evaluation metrics tell us about saliency models"
    - "recurrent mixture density network for spatiotemporal visual attention"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "recurrent mixture density network for spatiotemporal visual attention"):
  with members:
    - "end to end saliency mapping via probability distribution prediction"
    - "visual saliency based on multiscale deep features"
    - "beyond short snippets deep networks for video classification"
    - "attentionnet aggregating weak directions for accurate object detection"
    - "action recognition using visual attention"
    - "draw a recurrent neural network for image generation"
    - "end to end convolutional network for saliency prediction"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (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"
    - "learning gaze transitions from depth to improve video saliency estimation"
    - "visual saliency based on multiscale deep features"
    - "fixation prediction with a combined model of bottom up saliency and vanishing point"

Connected through hyperedge HE-5 (induced by source paper "saliency driven object recognition in egocentric videos with deep cnn"):
  with members:
    - "analyzing the performance of multilayer neural networks for object recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "pedestrian detection with unsupervised multi stage feature learning"

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)