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: "local aggregation for unsupervised learning of visual embeddings"
  Abstract: Unsupervised approaches to learning in neural networks are of substantial interest for furthering artificial intelligence, both because they would enable the training of networks without the need for large numbers of expensive annotations, and because they would be better models of the kind of general-purpose learning deployed by humans. However, unsupervised networks have long lagged behind the performance of their supervised counterparts, especially in the domain of large-scale visual recognition. Recent developments in training deep convolutional embeddings to maximize non-parametric instance separation and clustering objectives have shown promise in closing this gap. Here, we describe a method that trains an embedding function to maximize a metric of local aggregation, causing similar data instances to move together in the embedding space, while allowing dissimilar instances to separate. This aggregation metric is dynamic, allowing soft clusters of different scales to emerge. We evaluate our procedure on several large-scale visual recognition datasets, achieving state-of-the-art unsupervised transfer learning performance on object recognition in ImageNet, scene recognition in Places 205, and object detection in PASCAL VOC.

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 "convergence of end to end training in deep unsupervised contrasitive learning"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "how much over parameterization is sufficient to learn deep relu networks"
    - "contrastive multiview coding"
    - "an improved analysis of training over parameterized deep neural networks"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "momentum contrast for unsupervised visual representation learning"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"

Connected through hyperedge HE-3 (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"
    - "unsupervised pre training of image features on non curated data"
    - "bag of freebies for training object detection neural networks"

Connected through hyperedge HE-4 (induced by source paper "a simple framework for contrastive learning of visual representations"):
  with members:
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "on mutual information maximization for representation learning"
    - "contrastive multiview coding"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "data efficient image recognition with contrastive predictive coding"
    - "unsupervised data augmentation for consistency training"
    - "learning representations by maximizing mutual information across views"

Connected through hyperedge HE-5 (induced by source paper "evolving losses for unsupervised video representation learning"):
  with members:
    - "learning correspondence from the cycle consistency of time"
    - "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"
    - "self supervised video representation learning with space time cubic puzzles"

Connected through hyperedge HE-6 (induced by source paper "cross domain self supervised learning for domain adaptation with few source labels"):
  with members:
    - "universal domain adaptation through self supervision"
    - "drop to adapt learning discriminative features for unsupervised domain adaptation"
    - "unsupervised deep learning by neighbourhood discovery"
    - "confidence regularized self training"
    - "momentum contrast for unsupervised visual representation learning"
    - "domain generalization by solving jigsaw puzzles"
    - "bridging theory and algorithm for domain adaptation"
    - "semi supervised domain adaptation via minimax entropy"

Connected through hyperedge HE-7 (induced by source paper "universal domain adaptation through self supervision"):
  with members:
    - "unsupervised deep learning by neighbourhood discovery"
    - "momentum contrast for unsupervised visual representation learning"
    - "domain specific batch normalization for unsupervised domain adaptation"
    - "domain generalization by solving jigsaw puzzles"
    - "bridging theory and algorithm for domain adaptation"
    - "learning to transfer examples for partial domain adaptation"
    - "semi supervised domain adaptation via minimax entropy"
    - "cluster alignment with a teacher for unsupervised domain adaptation"

Connected through hyperedge HE-8 (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"
    - "unsupervised pre training of image features on non curated data"

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)