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 "rethinking image mixture for unsupervised visual representation learning"):
  Abstract: In supervised learning, smoothing label/prediction distribution in neural network training has been proven useful in preventing the model from being over-confident, and is crucial for learning more robust visual representations. This observation motivates us to explore the way to make predictions flattened in unsupervised learning. Considering that human annotated labels are not adopted in unsupervised learning, we introduce a straightforward approach to perturb input image space in order to soften the output prediction space indirectly. Despite its conceptual simplicity, we show empirically that with the simple solution -- image mixture, we can learn more robust visual representations from the transformed input, and the benefits of representations learned from this space can be inherited by the linear classification and downstream tasks.

Includes members:
  Member "a simple framework for contrastive learning of visual representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi modal self supervision from generalized data transformations")
  Member "improved baselines with momentum contrastive learning":
    also connected through other hyperedges:
      (none)
  Member "contrastive multiview coding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning representations by predicting bags of visual words")
  Member "when does label smoothing help":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")
  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 "large scale adversarial representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "unsupervised pre training of image features on non curated data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning representations by predicting bags of visual words")
  Member "bag of freebies for training object detection neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mixup regularization for region proposal based object detectors")

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)