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 "textboxes a fast text detector with a single deep neural network"):
  Abstract: This paper presents an end-to-end trainable fast scene text detector, named TextBoxes, which detects scene text with both high accuracy and efficiency in a single network forward pass, involving no post-process except for a standard non-maximum suppression. TextBoxes outperforms competing methods in terms of text localization accuracy and is much faster, taking only 0.09s per image in a fast implementation. Furthermore, combined with a text recognizer, TextBoxes significantly outperforms state-of-the-art approaches on word spotting and end-to-end text recognition tasks.

Includes members:
  Member "text flow a unified text detection system in natural scene images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploring the capacity of sequential free box discretization network for omnidirectional scene text detection")
  Member "multi oriented text detection with fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "looking glamorous vehicle re id in heterogeneous cameras networks with global and local attention")
  Member "ssd single shot multibox detector":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "synthetic data for text localisation in natural images":
    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 "deeptext a unified framework for text proposal generation and text detection in natural images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "abcnet real time scene text spotting with adaptive bezier curve network")
  Member "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "abcnet real time scene text spotting with adaptive bezier curve network")
  Member "you only look once unified real time object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "fast r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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)