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 "textcontournet a flexible and effective framework for improving scene text detection architecture with a multi task cascade"):
  Abstract: We study the problem of extracting text instance contour information from images and use it to assist scene text detection. We propose a novel and effective framework for this and experimentally demonstrate that: (1) A CNN that can be effectively used to extract instance-level text contour from natural images. (2) The extracted contour information can be used for better scene text detection. We propose two ways for learning the contour task together with the scene text detection: (1) as an auxiliary task and (2) as multi-task cascade. Extensive experiments with different benchmark datasets demonstrate that both designs improve the performance of a state-of-the-art scene text detector and that a multi-task cascade design achieves the best performance.

Includes members:
  Member "pixellink detecting scene text via instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "textboxes a single shot oriented scene text detector":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "arbitrary oriented object detection with circular smooth label")
  Member "towards end to end text spotting with convolutional recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "abcnet real time scene text spotting with adaptive bezier curve network")
  Member "east an efficient and accurate scene text detector":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "single shot text detector with regional attention":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "puzzlenet scene text detection by segment context graph learning")
  Member "richer convolutional features for edge detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cafenet class agnostic few shot edge detection network")
  Member "deep direct regression for multi oriented scene text detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "r2cnn rotational region cnn for orientation robust scene text detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "arbitrary oriented object detection with circular smooth label")

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)