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 "generating text sequence images for recognition"):
  Abstract: Recently, methods based on deep learning have dominated the field of text recognition. With a large number of training data, most of them can achieve the state-of-the-art performances. However, it is hard to harvest and label sufficient text sequence images from the real scenes. To mitigate this issue, several methods to synthesize text sequence images were proposed, yet they usually need complicated preceding or follow-up steps. In this work, we present a method which is able to generate infinite training data without any auxiliary pre/post-process. We tackle the generation task as an image-to-image translation one and utilize conditional adversarial networks to produce realistic text sequence images in the light of the semantic ones. Some evaluation metrics are involved to assess our method and the results demonstrate that the caliber of the data is satisfactory. The code and dataset will be publicly available soon.

Includes members:
  Member "what is wrong with scene text recognition model comparisons dataset and model analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "verisimilar image synthesis for accurate detection and recognition of texts in scenes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scene text recognition via transformer")
  Member "edit probability for scene text recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient backbone search for scene text recognition")
  Member "gans trained by a two time scale update rule converge to a nash equilibrium":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "focusing attention towards accurate text recognition in natural images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "recursive recurrent nets with attention modeling for ocr in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient backbone search for scene text recognition")
  Member "improved techniques for training gans":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "image to image translation with conditional adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)