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 "domain bridge for unpaired image to image translation and unsupervised domain adaptation"):
  Abstract: Image-to-image translation architectures may have limited effectiveness in some circumstances. For example, while generating rainy scenarios, they may fail to model typical traits of rain as water drops, and this ultimately impacts the synthetic images realism. With our method, called domain bridge, web-crawled data are exploited to reduce the domain gap, leading to the inclusion of previously ignored elements in the generated images. We make use of a network for clear to rain translation trained with the domain bridge to extend our work to Unsupervised Domain Adaptation (UDA). In that context, we introduce an online multimodal style-sampling strategy, where image translation multimodality is exploited at training time to improve performances. Finally, a novel approach for self-supervised learning is presented, and used to further align the domains. With our contributions, we simultaneously increase the realism of the generated images, while reaching on par performances with respect to the UDA state-of-the-art, with a simpler approach.

Includes members:
  Member "curriculum model adaptation with synthetic and real data for semantic foggy scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "night time semantic segmentation with a large real dataset")
  Member "i can see clearly now image restoration via de raining":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions")
  Member "towards instance level image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "one to one mapping for unpaired image to image translation")
  Member "physics based rendering for improving robustness to rain":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nuscenes a multimodal dataset for autonomous driving")
  Member "few shot unsupervised image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stylegan2 distillation for feed forward image manipulation")
  Member "bidirectional learning for domain adaptation of semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "harmonizing transferability and discriminability for adapting object detectors")
  Member "adversarial learning and self teaching techniques for domain adaptation in semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised domain adaptation for mobile semantic segmentation based on cycle consistency and feature alignment")
  Member "label propagation for deep semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "two cycle learning clustering based regularisation for deep semi supervised classification")

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)