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: "a simple framework for contrastive learning of visual representations"
  Abstract: This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels.

Connected through hyperedge HE-1 (induced by source paper "multi modal self supervision from generalized data transformations"):
  with members:
    - "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"
    - "data efficient image recognition with contrastive predictive coding"

Connected through hyperedge HE-2 (induced by source paper "convergence of end to end training in deep unsupervised contrasitive learning"):
  with members:
    - "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"
    - "global convergence of adaptive gradient methods for an over parameterized neural network"

Connected through hyperedge HE-3 (induced by source paper "rethinking image mixture for unsupervised visual representation learning"):
  with members:
    - "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"
    - "data efficient image recognition with contrastive predictive coding"

Connected through hyperedge HE-4 (induced by source paper "a metric learning reality check"):
  with members:
    - "revisiting training strategies and generalization performance in deep metric learning"
    - "unbiased evaluation of deep metric learning algorithms"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "hardness aware deep metric learning"
    - "momentum contrast for unsupervised visual representation learning"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "signal to noise ratio a robust distance metric for deep metric learning"
    - "softtriple loss deep metric learning without triplet sampling"

Connected through hyperedge HE-5 (induced by source paper "watching the world go by representation learning from unlabeled videos"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "contrastive multiview coding"
    - "momentum contrast for unsupervised visual representation learning"
    - "learning the depths of moving people by watching frozen people"
    - "learning correspondence from the cycle consistency of time"
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "self supervised learning of pretext invariant representations"

Connected through hyperedge HE-6 (induced by source paper "learning predictive representations for deformable objects using contrastive estimation"):
  with members:
    - "deep imitation learning of sequential fabric smoothing policies"
    - "contrastive multiview coding"
    - "learning robotic manipulation through visual planning and acting"
    - "learning to manipulate deformable objects without demonstrations"
    - "deep transfer learning of pick points on fabric for robot bed making"
    - "learning latent dynamics for planning from pixels"
    - "learning dexterous in hand manipulation"
    - "learning plannable representations with causal infogan"

Connected through hyperedge HE-7 (induced by source paper "vq draw a sequential discrete vae"):
  with members:
    - "generating diverse high fidelity images with vq vae 2"
    - "group normalization"
    - "representation learning with contrastive predictive coding"
    - "progressive growing of gans for improved quality stability and variation"
    - "neural discrete representation learning"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "variational inference for monte carlo objectives"
    - "training deep nets with sublinear memory cost"

Connected through hyperedge HE-8 (induced by source paper "improved baselines with momentum contrastive learning"):
  with members:
    - "momentum contrast for unsupervised visual representation learning"
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "unsupervised embedding learning via invariant and spreading instance feature"
    - "representation learning with contrastive predictive coding"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

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.LG(Machine Learning)