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 "spatial fusion gan for image synthesis"):
  Abstract: Recent advances in generative adversarial networks (GANs) have shown great potentials in realistic image synthesis whereas most existing works address synthesis realism in either appearance space or geometry space but few in both. This paper presents an innovative Spatial Fusion GAN (SF-GAN) that combines a geometry synthesizer and an appearance synthesizer to achieve synthesis realism in both geometry and appearance spaces. The geometry synthesizer learns contextual geometries of background images and transforms and places foreground objects into the background images unanimously. The appearance synthesizer adjusts the color, brightness and styles of the foreground objects and embeds them into background images harmoniously, where a guided filter is introduced for detail preserving. The two synthesizers are inter-connected as mutual references which can be trained end-to-end without supervision. The SF-GAN has been evaluated in two tasks: (1) realistic scene text image synthesis for training better recognition models; (2) glass and hat wearing for realistic matching glasses and hats with real portraits. Qualitative and quantitative comparisons with the state-of-the-art demonstrate the superiority of the proposed SF-GAN.

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 "st gan spatial transformer generative adversarial networks for image compositing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "verisimilar image synthesis for accurate detection and recognition of texts in scenes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scene text recognition via transformer")
  Member "compositional gan learning conditional image composition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive composition gan towards realistic image synthesis")
  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 "esir end to end scene text recognition via iterative image rectification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient backbone search for scene text recognition")
  Member "compositional gan learning image conditional binary composition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adaptive composition gan towards realistic image synthesis")
  Member "fast end to end trainable guided filter":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "predicting sharp and accurate occlusion boundaries in monocular depth estimation using displacement fields")

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)