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 "etnet error transition network for arbitrary style transfer"):
  Abstract: Numerous valuable efforts have been devoted to achieving arbitrary style transfer since the seminal work of Gatys et al. However, existing state-of-the-art approaches often generate insufficiently stylized results under challenging cases. We believe a fundamental reason is that these approaches try to generate the stylized result in a single shot and hence fail to fully satisfy the constraints on semantic structures in the content images and style patterns in the style images. Inspired by the works on error-correction, instead, we propose a self-correcting model to predict what is wrong with the current stylization and refine it accordingly in an iterative manner. For each refinement, we transit the error features across both the spatial and scale domain and invert the processed features into a residual image, with a network we call Error Transition Network (ETNet). The proposed model improves over the state-of-the-art methods with better semantic structures and more adaptive style pattern details. Various qualitative and quantitative experiments show that the key concept of both progressive strategy and error-correction leads to better results. Code and models are available at this https URL.

Includes members:
  Member "attention aware multi stroke style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "p 2 gan efficient style transfer using single style image")
  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 "learning linear transformations for fast arbitrary style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "imaginenet restyling apps using neural style transfer")
  Member "difnet semantic segmentation by diffusion networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "location aware upsampling for semantic segmentation")
  Member "style separation and synthesis via generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "from here to there video inbetweening using direct 3d convolutions")
  Member "deep back projection networks for super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gated fusion network for degraded image super resolution")
  Member "avatar net multi scale zero shot style transfer by feature decoration":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "regularized adaptation for stable and efficient continuous level learning on image processing networks")
  Member "nonlocal neural networks nonlocal diffusion and nonlocal modeling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "compact global descriptor for neural networks")

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)