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 "a taught obesrve ask toa method for object detection with critical supervision"):
  Abstract: Being inspired by child's learning experience - taught first and followed by observation and questioning, we investigate a critically supervised learning methodology for object detection in this work. Specifically, we propose a taught-observe-ask (TOA) method that consists of several novel components such as negative object proposal, critical example mining, and machine-guided question-answer (QA) labeling. To consider labeling time and performance jointly, new evaluation methods are developed to compare the performance of the TOA method, with the fully and weakly supervised learning methods. Extensive experiments are conducted on the PASCAL VOC and the Caltech benchmark datasets. The TOA method provides significantly improved performance of weakly supervision yet demands only about 3-6% of labeling time of full supervision. The effectiveness of each novel component is also analyzed.

Includes members:
  Member "extreme clicking for efficient object annotation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "multiple instance curriculum learning for weakly supervised object detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hard pixel mining for depth privileged semantic segmentation")
  Member "weakly supervised object localization using things and stuff transfer":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "mining object parts from cnns via active question answering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "interpretable cnns for object classification")
  Member "ron reverse connection with objectness prior networks for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pixel semantic revise of position learning a one stage object detector with a shared encoder decoder")
  Member "weakly and semi supervised object detection with expectation maximization algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ehsod cam guided end to end hybrid supervised object detection with cascade refinement")
  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 "training region based object detectors with online hard example mining":
    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)