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 adaptive faster r cnn for object detection in the wild"):
  Abstract: Object detection typically assumes that training and test data are drawn from an identical distribution, which, however, does not always hold in practice. Such a distribution mismatch will lead to a significant performance drop. In this work, we aim to improve the cross-domain robustness of object detection. We tackle the domain shift on two levels: 1) the image-level shift, such as image style, illumination, etc, and 2) the instance-level shift, such as object appearance, size, etc. We build our approach based on the recent state-of-the-art Faster R-CNN model, and design two domain adaptation components, on image level and instance level, to reduce the domain discrepancy. The two domain adaptation components are based on H-divergence theory, and are implemented by learning a domain classifier in adversarial training manner. The domain classifiers on different levels are further reinforced with a consistency regularization to learn a domain-invariant region proposal network (RPN) in the Faster R-CNN model. We evaluate our newly proposed approach using multiple datasets including Cityscapes, KITTI, SIM10K, etc. The results demonstrate the effectiveness of our proposed approach for robust object detection in various domain shift scenarios.

Includes members:
  Member "semantic foggy scene understanding with synthetic data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "seeing through fog without seeing fog deep multimodal sensor fusion in unseen adverse weather")
  Member "road reality oriented adaptation for semantic segmentation of urban scenes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")
  Member "when unsupervised domain adaptation meets tensor representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "from open set to closed set supervised spatial divide and conquer for object counting")
  Member "fine grained recognition in the wild a multi task domain adaptation approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")
  Member "learning to discover cross domain relations with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "unified deep supervised domain adaptation and generalization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "domain adaptive ensemble learning")
  Member "curriculum domain adaptation for semantic segmentation of urban scenes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)