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 "dual attention guided dropblock module for weakly supervised object localization"):
  Abstract: In this paper, we present a dual-attention guided dropblock module, and aim at learning the informative and complementary visual features for weakly supervised object localization (WSOL). The attention mechanism is extended to the task of WSOL, and design two types of attention modules to learn the discriminative features for better feature representations. Based on two types of attention mechanism, we propose a channel attention guided dropout (CAGD) and a spatial attention guided dropblock (SAGD). The CAGD ranks channel attention by a measure of importance and consider the top-k largest magnitude attentions as important ones. The SAGD can not only completely remove the information by erasing the contiguous regions of feature maps rather than individual pixels, but also simply distinguish the foreground objects and background regions to alleviate the attention misdirection. Extensive experiments demonstrate that the proposed method achieves new state-of-the-art localization accuracy on a challenging dataset.

Includes members:
  Member "attention based dropout layer for weakly supervised object localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rethinking the route towards weakly supervised object localization")
  Member "survey of dropout methods for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed learning of deep neural networks using independent subnet training")
  Member "self produced guidance for weakly supervised object localization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "starnet towards weakly supervised few shot detection and explainable few shot classification")
  Member "dropblock a regularization method for convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on feature normalization and data augmentation")
  Member "adversarial complementary learning for weakly supervised object localization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "explainable deep classification models for domain generalization")
  Member "cbam convolutional block attention module":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "dual attention network for scene segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "tell me where to look guided attention inference network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "explainable deep classification models for domain generalization")

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)