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 "handwritten chinese font generation with collaborative stroke refinement"):
  Abstract: Automatic character generation is an appealing solution for new typeface design, especially for Chinese typefaces including over 3700 most commonly-used characters. This task has two main pain points: (i) handwritten characters are usually associated with thin strokes of few information and complex structure which are error prone during deformation; (ii) thousands of characters with various shapes are needed to synthesize based on a few manually designed characters. To solve those issues, we propose a novel convolutional-neural-network-based model with three main techniques: collaborative stroke refinement, using collaborative training strategy to recover the missing or broken strokes; online zoom-augmentation, taking the advantage of the content-reuse phenomenon to reduce the size of training set; and adaptive pre-deformation, standardizing and aligning the characters. The proposed model needs only 750 paired training samples; no pre-trained network, extra dataset resource or labels is needed. Experimental results show that the proposed method significantly outperforms the state-of-the-art methods under the practical restriction on handwritten font synthesis.

Includes members:
  Member "learning discriminative video representations using adversarial perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition")
  Member "a style aware content loss for real time hd style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "collaborative distillation for ultra resolution universal style transfer")
  Member "building fast and compact convolutional neural networks for offline handwritten chinese character recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pioneer dataset and automatic recognition of urdu handwritten characters using a deep autoencoder and convolutional neural network")
  Member "auto encoder guided gan for chinese calligraphy synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ganwriting content conditioned generation of styled handwritten word images")
  Member "learning to write stylized chinese characters by reading a handful of examples":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "editing text in the wild")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "unsupervised diverse colorization via generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chromagan adversarial picture colorization with semantic class distribution")
  Member "multi content gan for few shot font style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "swaptext image based texts transfer in scenes")

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)