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 "exploiting web images for weakly supervised object detection"):
  Abstract: In recent years, the performance of object detection has advanced significantly with the evolving deep convolutional neural networks. However, the state-of-the-art object detection methods still rely on accurate bounding box annotations that require extensive human labelling. Object detection without bounding box annotations, i.e, weakly supervised detection methods, are still lagging far behind. As weakly supervised detection only uses image level labels and does not require the ground truth of bounding box location and label of each object in an image, it is generally very difficult to distill knowledge of the actual appearances of objects. Inspired by curriculum learning, this paper proposes an easy-to-hard knowledge transfer scheme that incorporates easy web images to provide prior knowledge of object appearance as a good starting point. While exploiting large-scale free web imagery, we introduce a sophisticated labour free method to construct a web dataset with good diversity in object appearance. After that, semantic relevance and distribution relevance are introduced and utilized in the proposed curriculum training scheme. Our end-to-end learning with the constructed web data achieves remarkable improvement across most object classes especially for the classes that are often considered hard in other works.

Includes members:
  Member "zero annotation object detection with web knowledge transfer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse and structured visual attention")
  Member "how hard can it be estimating the difficulty of visual search in an image":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "low budget unsupervised label query through domain alignment enforcement")
  Member "multiple instance curriculum learning for weakly supervised object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hard pixel mining for depth privileged semantic segmentation")
  Member "stc a simple to complex framework for weakly supervised semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised semantic segmentation by iterative affinity learning")
  Member "exploiting web images for dataset construction a domain robust approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "c rpns promoting object detection in real world via a cascade structure of region proposal networks")
  Member "weakly supervised object localization with multi fold multiple instance learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "multiple instance detection network with online instance classifier refinement":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weakly supervised instance segmentation by deep community learning")
  Member "ssd single shot multibox detector":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

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)