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 "arbitrary style transfer in real time with adaptive instance normalization"):
  Abstract: Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network.

Includes members:
  Member "batch renormalization towards reducing minibatch dependence in batch normalized models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rethinking batch normalization in transformers")
  Member "demystifying neural style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")
  Member "stable and controllable neural texture synthesis and style transfer using histogram losses":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "collaborative distillation for ultra resolution universal style transfer")
  Member "multi style generative network for real time transfer":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "domain adaptive few shot learning")
  Member "synthetic to real adaptation with generative correlation alignment networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial learning and self teaching techniques for domain adaptation in semantic segmentation")
  Member "diversified texture synthesis with feed forward networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "collaborative distillation for ultra resolution universal style transfer")
  Member "learning to discover cross domain relations with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "style transfer via texture synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gated gan adversarial gated networks for multi collection style transfer")

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)