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 "pair wise exchangeable feature extraction for arbitrary style transfer"):
  Abstract: Style transfer has been an important topic in both computer vision and graphics. Gatys et al. first prove that deep features extracted by the pre-trained VGG network represent both content and style features of an image and hence, style transfer can be achieved through optimization in feature space. Huang et al. then show that real-time arbitrary style transfer can be done by simply aligning the mean and variance of each feature channel. In this paper, however, we argue that only aligning the global statistics of deep features cannot always guarantee a good style transfer. Instead, we propose to jointly analyze the input image pair and extract common/exchangeable style features between the two. Besides, a new fusion mode is developed for combining content and style information in feature space. Qualitative and quantitative experiments demonstrate the advantages of our approach.

Includes members:
  Member "multi marginal wasserstein gan":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "intelligent home 3d automatic 3d house design from linguistic descriptions only")
  Member "attention aware multi stroke style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "p 2 gan efficient style transfer using single style image")
  Member "etnet error transition network for arbitrary style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efanet exchangeable feature alignment network for arbitrary style transfer")
  Member "adversarial learning with local coordinate coding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "arbitrary style transfer with style attentional networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "swaptext image based texts transfer in scenes")
  Member "a style aware content loss for real time hd style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "collaborative distillation for ultra resolution universal style transfer")
  Member "multimodal unsupervised image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "exemplar guided unsupervised image to image translation with semantic consistency":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object centric image generation from layouts")

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)