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 "separating content from style using adversarial learning for recognizing text in the wild"):
  Abstract: In this work we propose to improve text recognition from a new perspective by separating text content from complex backgrounds. We exploit the generative adversarial networks (GANs) for removing backgrounds while retaining the text content. As vanilla GANs are not sufficiently robust to generate sequence-like characters in natural images, we propose an adversarial learning framework for the generation and recognition of multiple characters in an image. The proposed framework consists of an attention-based recognizer and a generative adversarial architecture. Furthermore, to tackle the lack of paired training samples, we design an interactive joint training scheme, which shares attention masks from the recognizer to the discriminator, and enables the discriminator to extract the features of every character for further adversarial training. Benefiting from the character-level adversarial training, our framework requires only unpaired simple data for style supervision. Every target style sample containing only one randomly chosen character can be simply synthesized online during the training. This is significant as the training does not require costly paired samples or character-level annotations. Thus, only the input images and corresponding text labels are needed. In addition to the style transfer of the backgrounds, we refine character patterns to ease the recognition task. A feedback mechanism is proposed to bridge the gap between the discriminator and the recognizer. Therefore, the discriminator can guide the generator according to the confusion of the recognizer. The generated patterns are thus clearer for recognition. Experiments on various benchmarks, including both regular and irregular text, demonstrate that our method significantly reduces the difficulty of recognition. Our framework can be integrated with recent recognition methods to achieve new state-of-the-art performance.

Includes members:
  Member "controllable artistic text style transfer via shape matching gan":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "swaptext image based texts transfer in scenes")
  Member "scene text recognition from two dimensional perspective":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient backbone search for scene text recognition")
  Member "show attend and read a simple and strong baseline for irregular text recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient backbone search for scene text recognition")
  Member "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes":
    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 "began boundary equilibrium generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "neural style transfer a review":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "collaborative distillation for ultra resolution universal style transfer")
  Member "multi content gan for few shot font style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "swaptext image based texts transfer in scenes")
  Member "focusing attention towards accurate text recognition in natural images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")

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)