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 "lost a flexible framework for semi automatic image annotation"):
  Abstract: State-of-the-art computer vision approaches rely on huge amounts of annotated data. The collection of such data is a time consuming process since it is mainly performed by humans. The literature shows that semi-automatic annotation approaches can significantly speed up the annotation process by the automatic generation of annotation proposals to support the annotator. In this paper we present a framework that allows for a quick and flexible design of semi-automatic annotation pipelines. We show that a good design of the process will speed up the collection of annotations. Our contribution is a new approach to image annotation that allows for the combination of different annotation tools and machine learning algorithms in one process. We further present potential applications of our approach. The source code of our framework called LOST (Label Objects and Save Time) is available at: this https URL.

Includes members:
  Member "freelabel a publicly available annotation tool based on freehand traces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the vgg image annotator via")
  Member "efficient interactive annotation of segmentation datasets with polygon rnn":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "active learning for deep object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "active and incremental learning with weak supervision")
  Member "mask editor an image annotation tool for image segmentation tasks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the vgg image annotator via")
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "extreme clicking for efficient object annotation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "training object class detectors with click supervision":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards noise resistant object detection with noisy annotations")
  Member "we don t need no bounding boxes training object class detectors using only human verification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "towards noise resistant object detection with noisy annotations")

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)