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 "unsupervised pose flow learning for pose guided synthesis"):
  Abstract: Pose guided synthesis aims to generate a new image in an arbitrary target pose while preserving the appearance details from the source image. Existing approaches rely on either hard-coded spatial transformations or 3D body modeling. They often overlook complex non-rigid pose deformation or unmatched occluded regions, thus fail to effectively preserve appearance information. In this paper, we propose an unsupervised pose flow learning scheme that learns to transfer the appearance details from the source image. Based on such learned pose flow, we proposed GarmentNet and SynthesisNet, both of which use multi-scale feature-domain alignment for coarse-to-fine synthesis. Experiments on the DeepFashion, MVC dataset and additional real-world datasets demonstrate that our approach compares favorably with the state-of-the-art methods and generalizes to unseen poses and clothing styles.

Includes members:
  Member "dense intrinsic appearance flow for human pose transfer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")
  Member "soft gated warping gan for pose guided person image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "toward characteristic preserving image based virtual try on network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "garmentgan photo realistic adversarial fashion transfer")
  Member "spectral normalization for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "a variational u net for conditional appearance and shape generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "dense pose transfer":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")
  Member "the unreasonable effectiveness of deep features as a perceptual metric":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "m2e try on net fashion from model to everyone":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "end to end learning of geometric deformations of feature maps for virtual try on")

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)