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 "efanet exchangeable feature alignment network for arbitrary style transfer"):
  Abstract: Style transfer has been an important topic both in computer vision and graphics. Since the seminal work of Gatys et al. first demonstrates the power of stylization through optimization in the deep feature space, quite a few approaches have achieved real-time arbitrary style transfer with straightforward statistic matching techniques. In this work, our key observation is that only considering features in the input style image for the global deep feature statistic matching or local patch swap may not always ensure a satisfactory style transfer; see e.g., Figure 1. Instead, we propose a novel transfer framework, EFANet, that aims to jointly analyze and better align exchangeable features extracted from content and style image pair. In this way, the style features from the style image seek for the best compatibility with the content information in the content image, leading to more structured stylization results. In addition, a new whitening loss is developed for purifying the computed content features and better fusion with styles 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 "pair wise exchangeable feature extraction for arbitrary style transfer")
  Member "arbitrary style transfer with style attentional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (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-6 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "style separation and synthesis via generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "from here to there video inbetweening using direct 3d convolutions")
  Member "dida disentangled synthesis for domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "emerging disentanglement in auto encoder based unsupervised image content 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)