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 "style mixer semantic aware multi style transfer network"):
  Abstract: Recent neural style transfer frameworks have obtained astonishing visual quality and flexibility in Single-style Transfer (SST), but little attention has been paid to Multi-style Transfer (MST) which refers to simultaneously transferring multiple styles to the same image. Compared to SST, MST has the potential to create more diverse and visually pleasing stylization results. In this paper, we propose the first MST framework to automatically incorporate multiple styles into one result based on regional semantics. We first improve the existing SST backbone network by introducing a novel multi-level feature fusion module and a patch attention module to achieve better semantic correspondences and preserve richer style details. For MST, we designed a conceptually simple yet effective region-based style fusion module to insert into the backbone. It assigns corresponding styles to content regions based on semantic matching, and then seamlessly combines multiple styles together. Comprehensive evaluations demonstrate that our framework outperforms existing works of SST and MST.

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 "arbitrary style transfer with style attentional networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "swaptext image based texts transfer in scenes")
  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 "metastyle three way trade off among speed flexibility and quality in neural style transfer":
    also connected through other hyperedges:
      (none)
  Member "arbitrary style transfer with deep feature reshuffle":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parameter free style projection for arbitrary style transfer")
  Member "the contextual loss for image transformation with non aligned data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deformation aware unpaired image translation for pose estimation on laboratory animals")
  Member "avatar net multi scale zero shot style transfer by feature decoration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regularized adaptation for stable and efficient continuous level learning on image processing networks")
  Member "non local neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")

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)