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 pre training of image features on non curated data"
  Abstract: Pre-training general-purpose visual features with convolutional neural networks without relying on annotations is a challenging and important task. Most recent efforts in unsupervised feature learning have focused on either small or highly curated datasets like ImageNet, whereas using uncurated raw datasets was found to decrease the feature quality when evaluated on a transfer task. Our goal is to bridge the performance gap between unsupervised methods trained on curated data, which are costly to obtain, and massive raw datasets that are easily available. To that effect, we propose a new unsupervised approach which leverages self-supervision and clustering to capture complementary statistics from large-scale data. We validate our approach on 96 million images from YFCC100M, achieving state-of-the-art results among unsupervised methods on standard benchmarks, which confirms the potential of unsupervised learning when only uncurated data are available. We also show that pre-training a supervised VGG-16 with our method achieves 74.9% top-1 classification accuracy on the validation set of ImageNet, which is an improvement of +0.8% over the same network trained from scratch. Our code is available at this https URL.

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 "rethinking image mixture for unsupervised visual representation learning"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "improved baselines with momentum contrastive learning"
    - "contrastive multiview coding"
    - "when does label smoothing help"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "bag of freebies for training object detection neural networks"
    - "data efficient image recognition with contrastive predictive coding"

Connected through hyperedge HE-3 (induced by source paper "pruning convolutional neural networks with self supervision"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "linear mode connectivity and the lottery ticket hypothesis"
    - "contrastive multiview coding"
    - "data efficient image recognition with contrastive predictive coding"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "learning representations by maximizing mutual information across views"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-4 (induced by source paper "end to end learning of visual representations from uncurated instructional videos"):
  with members:
    - "a short note on the kinetics 700 human action dataset"
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "learning video representations using contrastive bidirectional transformer"
    - "contrastive multiview coding"
    - "mining youtube a dataset for learning fine grained action concepts from webly supervised video data"
    - "a case study on combining asr and visual features for generating instructional video captions"
    - "toward self supervised object detection in unlabeled videos"
    - "multimodal abstractive summarization for how2 videos"

Connected through hyperedge HE-5 (induced by source paper "self supervised learning of pretext invariant representations"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "s4l self supervised semi supervised learning"
    - "learning video representations using contrastive bidirectional transformer"
    - "contrastive multiview coding"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "domain generalization by solving jigsaw puzzles"

Connected through hyperedge HE-6 (induced by source paper "refineloc iterative refinement for weakly supervised action localization"):
  with members:
    - "3c net category count and center loss for weakly supervised action localization"
    - "slim densepose thrifty learning from sparse annotations and motion cues"
    - "ms tcn multi stage temporal convolutional network for action segmentation"
    - "weakly supervised action localization with background modeling"
    - "bmn boundary matching network for temporal action proposal generation"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "deep learning is robust to massive label noise"
    - "guess where actor supervision for spatiotemporal action localization"

Connected through hyperedge HE-7 (induced by source paper "clusterfit improving generalization of visual representations"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "billion scale semi supervised learning for image classification"
    - "large scale weakly supervised pre training for video action recognition"
    - "revisiting self supervised visual representation learning"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "large scale distributed neural network training through online distillation"
    - "learning to separate object sounds by watching unlabeled video"

Connected through hyperedge HE-8 (induced by source paper "when does self supervision improve few shot learning"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "surprising effectiveness of few image unsupervised feature learning"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "boosting few shot visual learning with self supervision"
    - "large scale adversarial representation learning"
    - "domain generalization by solving jigsaw puzzles"
    - "data efficient image recognition with contrastive predictive coding"

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)