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: "the 2018 davis challenge on video object segmentation"
  Abstract: We present the 2018 DAVIS Challenge on Video Object Segmentation, a public competition specifically designed for the task of video object segmentation. It builds upon the DAVIS 2017 dataset, which was presented in the previous edition of the DAVIS Challenge, and added 100 videos with multiple objects per sequence to the original DAVIS 2016 dataset. Motivated by the analysis of the results of the 2017 edition, the main track of the competition will be the same than in the previous edition (segmentation given the full mask of the objects in the first frame -- semi-supervised scenario). This edition, however, also adds an interactive segmentation teaser track, where the participants will interact with a web service simulating the input of a human that provides scribbles to iteratively improve the result.

Connected through hyperedge HE-1 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos"):
  with members:
    - "zero shot video object segmentation via attentive graph neural networks"
    - "key instance selection for unsupervised video object segmentation"
    - "large scale object mining for object discovery from unlabeled video"
    - "an efficient 3d cnn for action object segmentation in video"
    - "video instance segmentation"
    - "video object segmentation using space time memory networks"
    - "the 2019 davis challenge on vos unsupervised multi object segmentation"
    - "dmm net differentiable mask matching network for video object segmentation"

Connected through hyperedge HE-2 (induced by source paper "learning based human segmentation and velocity estimation using automatic labeled lidar sequence for training"):
  with members:
    - "automatic labeled lidar data generation based on precise human model"
    - "slowfast networks for video recognition"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "playing for data ground truth from computer games"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "mots multi object tracking and segmentation"):
  with members:
    - "panoptic segmentation"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "how transferable are the datasets collected by active learners"
    - "practical obstacles to deploying active learning"
    - "combined image and world space tracking in traffic scenes"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-4 (induced by source paper "video object segmentation and tracking a survey"):
  with members:
    - "interactive video object segmentation in the wild"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "maskrnn instance level video object segmentation"
    - "videomatch matching based video object segmentation"
    - "unsupervised video object segmentation for deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "ranet ranking attention network for fast video object segmentation"):
  with members:
    - "zero shot video object segmentation via attentive graph neural networks"
    - "rethinking rgb d salient object detection models datasets and large scale benchmarks"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "egnet edge guidance network for salient object detection"
    - "fast and accurate online video object segmentation via tracking parts"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "videomatch matching based video object segmentation"

Connected through hyperedge HE-6 (induced by source paper "learning multi object tracking and segmentation from automatic annotations"):
  with members:
    - "video instance segmentation"
    - "disentangling monocular 3d object detection"
    - "seamless scene segmentation"
    - "mots multi object tracking and segmentation"
    - "panoptic deeplab"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "combined image and world space tracking in traffic scenes"

Connected through hyperedge HE-7 (induced by source paper "value of temporal dynamics information in driving scene segmentation"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "youtube vos sequence to sequence video object segmentation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the 2017 davis challenge on video object segmentation"
    - "learning video object segmentation with visual memory"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "full resolution residual networks for semantic segmentation in street scenes"

Connected through hyperedge HE-8 (induced by source paper "the 2019 davis challenge on vos unsupervised multi object segmentation"):
  with members:
    - "towards segmenting everything that moves"
    - "rvos end to end recurrent network for video object segmentation"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "deep spatio temporal random fields for efficient video segmentation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"

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)