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 "prior based domain adaptive object detection for adverse weather conditions"):
  Abstract: Adverse weather conditions such as rain and haze corrupt the quality of captured images, which cause detection networks trained on clean images to perform poorly on these images. To address this issue, we propose an unsupervised prior-based domain adversarial object detection framework for adapting the detectors to different weather conditions. We make the observations that corruptions due to different weather conditions (i) follow the principles of physics and hence, can be mathematically modeled, and (ii) often cause degradations in the feature space leading to deterioration in the detection performance. Motivated by these, we propose to use weather-specific prior knowledge obtained using the principles of image formation to define a novel prior-adversarial loss. The prior-adversarial loss used to train the adaptation process aims to produce weather-invariant features by reducing the weather-specific information in the features, thereby mitigating the effects of weather on the detection performance. Additionally, we introduce a set of residual feature recovery blocks in the object detection pipeline to de-distort the feature space, resulting in further improvements. The proposed framework outperforms all existing methods by a large margin when evaluated on different datasets such as Foggy-Cityscapes, Rainy-Cityscapes, RTTS and UFDD.

Includes members:
  Member "exploring object relation in mean teacher for cross domain detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "unbiased mean teacher for cross domain object detection")
  Member "single image deraining a comprehensive benchmark analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments")
  Member "automatic adaptation of object detectors to new domains using self training":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unbiased mean teacher for cross domain object detection")
  Member "diversify and match a domain adaptive representation learning paradigm for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "harmonizing transferability and discriminability for adapting object detectors")
  Member "vision meets drones a challenge":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "panda a gigapixel level human centric video dataset")
  Member "semantic foggy scene understanding with synthetic data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "seeing through fog without seeing fog deep multimodal sensor fusion in unseen adverse weather")
  Member "domain adaptive faster r cnn for object detection in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")

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)