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 deep ranking model for spatio temporal highlight detection from a 360 video"
  Abstract: We address the problem of highlight detection from a 360 degree video by summarizing it both spatially and temporally. Given a long 360 degree video, we spatially select pleasantly-looking normal field-of-view (NFOV) segments from unlimited field of views (FOV) of the 360 degree video, and temporally summarize it into a concise and informative highlight as a selected subset of subshots. We propose a novel deep ranking model named as Composition View Score (CVS) model, which produces a spherical score map of composition per video segment, and determines which view is suitable for highlight via a sliding window kernel at inference. To evaluate the proposed framework, we perform experiments on the Pano2Vid benchmark dataset and our newly collected 360 degree video highlight dataset from YouTube and Vimeo. Through evaluation using both quantitative summarization metrics and user studies via Amazon Mechanical Turk, we demonstrate that our approach outperforms several state-of-the-art highlight detection methods. We also show that our model is 16 times faster at inference than AutoCam, which is one of the first summarization algorithms of 360 degree videos

Connected through hyperedge HE-1 (induced by source paper "360 indoor towards learning real world objects in 360 deg indoor equirectangular images"):
  with members:
    - "cube padding for weakly supervised saliency prediction in 360 deg videos"
    - "yolov3 an incremental improvement"
    - "learning spherical convolution for fast features from 360 deg imagery"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "learning so 3 equivariant representations with spherical cnns"
    - "making 360 circ video watchable in 2d learning videography for click free viewing"
    - "self view grounding given a narrated 360 deg video"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "kernel transformer networks for compact spherical convolution"):
  with members:
    - "cube padding for weakly supervised saliency prediction in 360 deg videos"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "active convolution learning the shape of convolution for image classification"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deformable convolutional networks"
    - "learning spherical convolution for fast features from 360 deg imagery"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "learning so 3 equivariant representations with spherical cnns"

Connected through hyperedge HE-3 (induced by source paper "cube padding for weakly supervised saliency prediction in 360 deg videos"):
  with members:
    - "end to end saliency mapping via probability distribution prediction"
    - "saltinet scan path prediction on 360 degree images using saliency volumes"
    - "viraliency pooling local virality"
    - "the 2017 davis challenge on video object segmentation"
    - "learning spherical convolution for fast features from 360 deg imagery"
    - "semantic driven generation of hyperlapse from 360 circ video"
    - "learning video object segmentation with visual memory"
    - "salnet360 saliency maps for omni directional images with cnn"

Connected through hyperedge HE-4 (induced by source paper "a memory network approach for story based temporal summarization of 360 deg videos"):
  with members:
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "a read write memory network for movie story understanding"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "making 360 circ video watchable in 2d learning videography for click free viewing"
    - "learning visual storylines with skipping recurrent neural networks"
    - "summary transfer exemplar based subset selection for video summarization"
    - "dynamic neural turing machine with soft and hard addressing schemes"
    - "video summarization with long short term memory"

Connected through hyperedge HE-5 (induced by source paper "phd gifs personalized highlight detection for automatic gif creation"):
  with members:
    - "predicting visual context for unsupervised event segmentation in continuous photo streams"
    - "query adaptive video summarization via quality aware relevance estimation"
    - "self normalizing neural networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "ridiculously fast shot boundary detection with fully convolutional neural networks"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "query focused extractive video summarization"

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)