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 "esir end to end scene text recognition via iterative image rectification"):
  Abstract: Automated recognition of texts in scenes has been a research challenge for years, largely due to the arbitrary variation of text appearances in perspective distortion, text line curvature, text styles and different types of imaging artifacts. The recent deep networks are capable of learning robust representations with respect to imaging artifacts and text style changes, but still face various problems while dealing with scene texts with perspective and curvature distortions. This paper presents an end-to-end trainable scene text recognition system (ESIR) that iteratively removes perspective distortion and text line curvature as driven by better scene text recognition performance. An innovative rectification network is developed which employs a novel line-fitting transformation to estimate the pose of text lines in scenes. In addition, an iterative rectification pipeline is developed where scene text distortions are corrected iteratively towards a fronto-parallel view. The ESIR is also robust to parameter initialization and the training needs only scene text images and word-level annotations as required by most scene text recognition systems. Extensive experiments over a number of public datasets show that the proposed ESIR is capable of rectifying scene text distortions accurately, achieving superior recognition performance for both normal scene text images and those suffering from perspective and curvature distortions.

Includes members:
  Member "scene text synthesis for efficient and effective deep network training":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ga dan geometry aware domain adaptation network for scene text detection and recognition")
  Member "towards robust curve text detection with conditional spatial expansion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "an end to end textspotter with explicit alignment and attention":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "abcnet real time scene text spotting with adaptive bezier curve network")
  Member "verisimilar image synthesis for accurate detection and recognition of texts in scenes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scene text recognition via transformer")
  Member "accurate scene text detection through border semantics awareness and bootstrapping":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep relational reasoning graph network for arbitrary shape text detection")
  Member "edit probability for scene text recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient backbone search for scene text recognition")
  Member "spatial fusion gan for image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "swaptext image based texts transfer in scenes")
  Member "learning markov clustering networks for scene text detection":
    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)