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 "d textbf s 3 l deep self semi supervised learning for image recognition"):
  Abstract: Despite the recent progress in deep semi-supervised learning (Semi-SL), the amount of labels still plays a dominant role. The success in self-supervised learning (Self-SL) hints a promising direction to exploit the vast unlabeled data by leveraging an additional set of deterministic labels. In this paper, we propose Deep Self-Semi-Supervised learning (D$S^3$L), a flexible multi-task framework with shared parameters that integrates the rotation task in Self-SL with the consistency-based methods in deep Semi-SL. Our method is easy to implement and is complementary to all consistency-based approaches. The experiments demonstrate that our method significantly improves over the published state-of-the-art methods on several standard benchmarks, especially when fewer labels are presented.

Includes members:
  Member "small data challenges in big data era a survey of recent progress on unsupervised and semi supervised methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on semi self and unsupervised techniques in image classification")
  Member "s4l self supervised semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning representations by predicting bags of visual words")
  Member "a meta approach to defend noisy labels by the manifold regularizer psdr":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "countering noisy labels by learning from auxiliary clean labels")
  Member "unsupervised label noise modeling and loss correction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  Member "pushing the right boundaries matters wasserstein adversarial training for label noise":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "countering noisy labels by learning from auxiliary clean labels")
  Member "weight standardization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rethinking batch normalization in transformers")
  Member "understanding and utilizing deep neural networks trained with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  Member "revisiting self supervised visual representation learning":
    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)