human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "large scale learning of general visual representations for transfer"):
  Abstract: Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision. We revisit the paradigm of pre-training on large supervised datasets and fine-tuning the weights on the target task. We scale up pre-training, and create a simple recipe that we call Big Transfer (BiT). By combining a few carefully selected components, and transferring using a simple heuristic, we achieve strong performance on over 20 datasets. BiT performs well across a surprisingly wide range of data regimes - from 10 to 1M labeled examples. BiT achieves 87.8% top-1 accuracy on ILSVRC-2012, 99.3% on CIFAR-10, and 76.7% on the Visual Task Adaptation Benchmark (which includes 19 tasks). On small datasets, BiT attains 86.4% on ILSVRC-2012 with 25 examples per class, and 97.6% on CIFAR-10 with 10 examples per class. We conduct detailed analysis of the main components that lead to high transfer performance.

Includes members:
  Member "self supervised learning of video induced visual invariances":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automatic shortcut removal for self supervised representation learning")
  Member "cutmix regularization strategy to train strong classifiers with localizable features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning representations by predicting bags of visual words")
  Member "billion scale semi supervised learning for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "momentum contrast for unsupervised visual representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning representations by predicting bags of visual words")
  Member "an exponential learning rate schedule for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "do we train on test data purging cifar of near duplicates":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "understanding isomorphism bias in graph data sets")
  Member "the visual task adaptation benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "data efficient image recognition with contrastive predictive coding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning representations by predicting bags of visual words")

Question: Please tell me which class the source paper 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)