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 "scl towards accurate domain adaptive object detection via gradient detach based stacked complementary losses"):
  Abstract: Unsupervised domain adaptive object detection aims to learn a robust detector in the domain shift circumstance, where the training (source) domain is label-rich with bounding box annotations, while the testing (target) domain is label-agnostic and the feature distributions between training and testing domains are dissimilar or even totally different. In this paper, we propose a gradient detach based stacked complementary losses (SCL) method that uses detection losses as the primary objective, and cuts in several auxiliary losses in different network stages accompanying with gradient detach training to learn more discriminative representations. We argue that the prior methods mainly leverage more loss functions for training but ignore the interaction of different losses and also the compatible training strategy (gradient detach updating in our work). Thus, our proposed method is a more syncretic adaptation learning process. We conduct comprehensive experiments on seven datasets, the results demonstrate that our method performs favorably better than the state-of-the-art methods by a significant margin. For instance, from Cityscapes to FoggyCityscapes, we achieve 37.9% mAP, outperforming the previous art Strong-Weak by 3.6%.

Includes members:
  Member "self training and adversarial background regularization for unsupervised domain adaptive one stage object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "harmonizing transferability and discriminability for adapting object detectors")
  Member "towards universal object detection by domain attention":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "continual universal object detection")
  Member "multi adversarial faster rcnn for unrestricted object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "harmonizing transferability and discriminability for adapting object detectors")
  Member "exploring object relation in mean teacher for cross domain detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "harmonizing transferability and discriminability for adapting object detectors")
  Member "domain adaptation with asymmetrically relaxed distribution alignment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "alignflow cycle consistent learning from multiple domains via normalizing flows")
  Member "complement objective training":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a balanced and uncertainty aware approach for partial domain adaptation")
  Member "diversify and match a domain adaptive representation learning paradigm for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "harmonizing transferability and discriminability for adapting object detectors")
  Member "on learning invariant representation for domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "domain adaptation with conditional distribution matching and generalized label shift")

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)