human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "training object detectors from few weakly labeled and many unlabeled images"):
  with members:
    - "weakly supervised object detection with segmentation collaboration"
    - "object aware instance labeling for weakly supervised object detection"
    - "few shot object detection with attention rpn and multi relation detector"
    - "few example object detection with model communication"
    - "feature weighting and boosting for few shot segmentation"
    - "label propagation for deep semi supervised learning"
    - "adaptive masked proxies for few shot segmentation"
    - "mixmatch a holistic approach to semi supervised learning"

Connected through hyperedge HE-2 (induced by source paper "detecting 11k classes large scale object detection without fine grained bounding boxes"):
  with members:
    - "yolov3 an incremental improvement"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "sniper efficient multi scale training"
    - "soft sampling for robust object detection"
    - "soft nms improving object detection with one line of code"
    - "zero annotation object detection with web knowledge transfer"
    - "extreme clicking for efficient object annotation"
    - "revisiting knowledge transfer for training object class detectors"

Connected through hyperedge HE-3 (induced by source paper "zero annotation object detection with web knowledge transfer"):
  with members:
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "automatic discovery association estimation and learning of semantic attributes for a thousand categories"
    - "deep self taught learning for weakly supervised object localization"
    - "adversarial discriminative domain adaptation"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "multiple instance detection network with online instance classifier refinement"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"

Question: Please tell me which class the center node 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)