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 "training object class detectors with click supervision"):
  Abstract: Training object class detectors typically requires a large set of images with objects annotated by bounding boxes. However, manually drawing bounding boxes is very time consuming. In this paper we greatly reduce annotation time by proposing center-click annotations: we ask annotators to click on the center of an imaginary bounding box which tightly encloses the object instance. We then incorporate these clicks into existing Multiple Instance Learning techniques for weakly supervised object localization, to jointly localize object bounding boxes over all training images. Extensive experiments on PASCAL VOC 2007 and MS COCO show that: (1) our scheme delivers high-quality detectors, performing substantially better than those produced by weakly supervised techniques, with a modest extra annotation effort; (2) these detectors in fact perform in a range close to those trained from manually drawn bounding boxes; (3) as the center-click task is very fast, our scheme reduces total annotation time by 9x to 18x.

Includes members:
  Member "transfer learning by ranking for weakly supervised object annotation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "m adda unsupervised domain adaptation with deep metric learning")
  Member "weakly supervised object localization with multi fold multiple instance learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "click carving segmenting objects in video with point clicks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to track any object")
  Member "spot on action localization from pointly supervised proposals":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sf net single frame supervision for temporal action localization")
  Member "we don t need no bounding boxes training object class detectors using only human verification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards noise resistant object detection with noisy annotations")
  Member "track and transfer watching videos to simulate strong human supervision for weakly supervised object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "few example object detection with model communication")
  Member "embracing error to enable rapid crowdsourcing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "simple and effective regularization methods for training on noisily labeled data with generalization guarantee")
  Member "contextlocnet context aware deep network models for weakly supervised localization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ha ccn hierarchical attention based crowd counting network")

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)