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: "unsupervised representation learning by sorting sequences"
  Abstract: We present an unsupervised representation learning approach using videos without semantic labels. We leverage the temporal coherence as a supervisory signal by formulating representation learning as a sequence sorting task. We take temporally shuffled frames (i.e., in non-chronological order) as inputs and train a convolutional neural network to sort the shuffled sequences. Similar to comparison-based sorting algorithms, we propose to extract features from all frame pairs and aggregate them to predict the correct order. As sorting shuffled image sequence requires an understanding of the statistical temporal structure of images, training with such a proxy task allows us to learn rich and generalizable visual representation. We validate the effectiveness of the learned representation using our method as pre-training on high-level recognition problems. The experimental results show that our method compares favorably against state-of-the-art methods on action recognition, image classification and object detection tasks.

Connected through hyperedge HE-1 (induced by source paper "learning representations by predicting bags of visual words"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "s4l self supervised semi supervised learning"
    - "when does self supervision improve few shot learning"
    - "contrastive multiview coding"
    - "boosting few shot visual learning with self supervision"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "momentum contrast for unsupervised visual representation learning"

Connected through hyperedge HE-2 (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-3 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning"):
  with members:
    - "end to end learning of visual representations from uncurated instructional videos"
    - "use what you have video retrieval using representations from collaborative experts"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "videobert a joint model for video and language representation learning"
    - "grounding object detections with transcriptions"
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "learning to detect and retrieve objects from unlabeled videos"

Connected through hyperedge HE-4 (induced by source paper "learning spatiotemporal features via video and text pair discrimination"):
  with members:
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "contrastive multiview coding"
    - "universal dependency parsing from scratch"
    - "dynamonet dynamic action and motion network"
    - "learning correspondence from the cycle consistency of time"
    - "videobert a joint model for video and language representation learning"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"

Connected through hyperedge HE-5 (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-6 (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-7 (induced by source paper "automatic shortcut removal for self supervised representation learning"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "on mutual information maximization for representation learning"
    - "self supervised learning of video induced visual invariances"
    - "contrastive multiview coding"
    - "momentum contrast for unsupervised visual representation learning"
    - "data efficient image recognition with contrastive predictive coding"
    - "adversarial examples improve image recognition"
    - "learning representations by maximizing mutual information across views"

Connected through hyperedge HE-8 (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"

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)