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: "self supervised video representation learning with space time cubic puzzles"
  Abstract: Self-supervised tasks such as colorization, inpainting and zigsaw puzzle have been utilized for visual representation learning for still images, when the number of labeled images is limited or absent at all. Recently, this worthwhile stream of study extends to video domain where the cost of human labeling is even more expensive. However, the most of existing methods are still based on 2D CNN architectures that can not directly capture spatio-temporal information for video applications. In this paper, we introduce a new self-supervised task called as \textit{Space-Time Cubic Puzzles} to train 3D CNNs using large scale video dataset. This task requires a network to arrange permuted 3D spatio-temporal crops. By completing \textit{Space-Time Cubic Puzzles}, the network learns both spatial appearance and temporal relation of video frames, which is our final goal. In experiments, we demonstrate that our learned 3D representation is well transferred to action recognition tasks, and outperforms state-of-the-art 2D CNN-based competitors on UCF101 and HMDB51 datasets.

Connected through hyperedge HE-1 (induced by source paper "mast a memory augmented self supervised tracker"):
  with members:
    - "video object segmentation using space time memory networks"
    - "self supervised learning for video correspondence flow"
    - "multigrid predictive filter flow for unsupervised learning on videos"
    - "learning correspondence from the cycle consistency of time"
    - "joint task self supervised learning for temporal correspondence"
    - "unsupervised deep tracking"
    - "rvos end to end recurrent network for video object segmentation"
    - "feelvos fast end to end embedding learning for video object segmentation"

Connected through hyperedge HE-2 (induced by source paper "multi modal self supervision from generalized data transformations"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "audiovisual slowfast networks for video recognition"
    - "learning spatiotemporal features via video and text pair discrimination"
    - "on mutual information maximization for representation learning"
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "end to end learning of visual representations from uncurated instructional videos"
    - "learning video representations using contrastive bidirectional transformer"
    - "contrastive multiview coding"

Connected through hyperedge HE-3 (induced by source paper "evolving losses for unsupervised video representation learning"):
  with members:
    - "learning correspondence from the cycle consistency of time"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "moments in time dataset one million videos for event understanding"
    - "deep clustering for unsupervised learning of visual features"
    - "taskonomy disentangling task transfer learning"
    - "unsupervised learning via meta learning"
    - "autoaugment learning augmentation policies from data"
    - "slowfast networks for video recognition"

Connected through hyperedge HE-4 (induced by source paper "deep self supervised representation learning for free hand sketch"):
  with members:
    - "deep learning for free hand sketch a survey"
    - "zero reference deep curve estimation for low light image enhancement"
    - "generative adversarial zero shot relational learning for knowledge graphs"
    - "the devil is in the channels mutual channel loss for fine grained image classification"
    - "multi graph transformer for free hand sketch recognition"
    - "learning for multi model and multi type fitting"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-5 (induced by source paper "videossl semi supervised learning for video classification"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "only time can tell discovering temporal data for temporal modeling"
    - "distinit learning video representations without a single labeled video"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "learning with privileged information via adversarial discriminative modality distillation"
    - "video representation learning by dense predictive coding"
    - "discrimnet semi supervised action recognition from videos using generative adversarial networks"
    - "semi supervised convolutional neural networks for human activity recognition"

Connected through hyperedge HE-6 (induced by source paper "self supervised spatio temporal representation learning using variable playback speed prediction"):
  with members:
    - "video cloze procedure for self supervised spatio temporal learning"
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "unsupervised domain adaptation through self supervision"
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "self supervised gans via auxiliary rotation loss"
    - "group normalization"
    - "unsupervised representation learning by predicting image rotations"
    - "slowfast networks for video recognition"

Connected through hyperedge HE-7 (induced by source paper "action segmentation with joint self supervised temporal domain adaptation"):
  with members:
    - "temporal attentive alignment for video domain adaptation"
    - "temporal attentive alignment for large scale video domain adaptation"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "ms tcn multi stage temporal convolutional network for action segmentation"
    - "attending to discriminative certainty for domain adaptation"
    - "human action recognition and prediction a survey"
    - "learning motion in feature space locally consistent deformable convolution networks for fine grained action detection"
    - "multi adversarial domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "video cloze procedure for self supervised spatio temporal learning"):
  with members:
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "learning image representations by completing damaged jigsaw puzzles"
    - "unsupervised representation learning by predicting image rotations"
    - "transitive invariance for self supervised visual representation learning"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "colorization as a proxy task for visual understanding"
    - "look listen and learn"

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)