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: "scaling and benchmarking self supervised visual representation learning"
  Abstract: Self-supervised learning aims to learn representations from the data itself without explicit manual supervision. Existing efforts ignore a crucial aspect of self-supervised learning - the ability to scale to large amount of data because self-supervision requires no manual labels. In this work, we revisit this principle and scale two popular self-supervised approaches to 100 million images. We show that by scaling on various axes (including data size and problem 'hardness'), one can largely match or even exceed the performance of supervised pre-training on a variety of tasks such as object detection, surface normal estimation (3D) and visual navigation using reinforcement learning. Scaling these methods also provides many interesting insights into the limitations of current self-supervised techniques and evaluations. We conclude that current self-supervised methods are not 'hard' enough to take full advantage of large scale data and do not seem to learn effective high level semantic representations. We also introduce an extensive benchmark across 9 different datasets and tasks. We believe that such a benchmark along with comparable evaluation settings is necessary to make meaningful progress. Code is at: this https URL.

Connected through hyperedge HE-1 (induced by source paper "learning representations by predicting bags of visual words"):
  with members:
    - "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"
    - "using self supervised learning can improve model robustness and uncertainty"

Connected through hyperedge HE-2 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark"):
  with members:
    - "on mutual information maximization for representation learning"
    - "large scale adversarial representation learning"
    - "classification accuracy score for conditional generative models"
    - "data efficient image recognition with contrastive predictive coding"
    - "do imagenet classifiers generalize to imagenet"
    - "remixmatch semi supervised learning with distribution alignment and augmentation anchoring"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-3 (induced by source paper "an exploration of embodied visual exploration"):
  with members:
    - "where to look next unsupervised active visual exploration on 360 input"
    - "embodied visual recognition"
    - "the replica dataset a digital replica of indoor spaces"
    - "emergence of exploratory look around behaviors through active observation completion"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "learning exploration policies for navigation"
    - "sidekick policy learning for active visual exploration"

Connected through hyperedge HE-4 (induced by source paper "automatic shortcut removal for self supervised representation learning"):
  with members:
    - "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"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-5 (induced by source paper "pruning convolutional neural networks with self supervision"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "linear mode connectivity and the lottery ticket hypothesis"
    - "contrastive multiview coding"
    - "unsupervised pre training of image features on non curated data"
    - "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-6 (induced by source paper "self supervised contextual bandits in computer vision"):
  with members:
    - "exploiting unlabeled data in cnns by self supervised learning to rank"
    - "self supervised exploration via disagreement"
    - "boosting few shot visual learning with self supervision"
    - "semi supervised learning with scarce annotations"
    - "deep clustering for unsupervised learning of visual features"
    - "two stream self supervised learning for action recognition"
    - "large scale study of curiosity driven learning"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-7 (induced by source paper "few shot learning via learning the representation provably"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "meta learning with differentiable convex optimization"
    - "online meta learning"
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "meta learning with differentiable closed form solvers"
    - "revisiting unreasonable effectiveness of data in deep learning era"

Connected through hyperedge HE-8 (induced by source paper "learning word referent mappings and concepts from raw inputs"):
  with members:
    - "mutual exclusivity as a challenge for deep neural networks"
    - "objects that sound"
    - "attention is all you need"
    - "decoupled weight decay regularization"
    - "building machines that learn and think like people"

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)