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 "unbiased mean teacher for cross domain object detection"):
  Abstract: Cross domain object detection is challenging, because object detection model is often vulnerable to data variance, especially to the considerable domain shift in cross domain scenarios. In this paper, we propose a new approach called Unbiased Mean Teacher (UMT) for cross domain object detection. While the simple mean teacher (MT) model exhibits good robustness to small data variance, it can also become easily biased in cross domain scenarios. We thus improve it with several simple yet highly effective strategies. In particular, we firstly propose a novel cross domain distillation for MT to maximally exploit the expertise of the teacher model. Then, we further alleviate the bias in the student model by augmenting training samples with pixel-level adaptation. The feature level adversarial training is also incorporated to learn domain-invariant representation. Those strategies can be implemented easily into MT and leads to our unbiased MT model. Our model surpasses the existing state-of-the-art models in large margins on benchmark datasets, which demonstrates the effectiveness of our approach.

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 "scl towards accurate domain adaptive object detection via gradient detach based stacked complementary losses":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep domain adaptive object detection a survey")
  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 "a robust learning approach to domain adaptive object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep domain adaptive object detection a survey")
  Member "automatic adaptation of object detectors to new domains using self training":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ifan image instance full alignment networks for adaptive object detection")
  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 "wasserstein distance based domain adaptation for object detection":
    also connected through other hyperedges:
      (none)

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)