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 "deep image spatial transformation for person image generation"):
  Abstract: Pose-guided person image generation is to transform a source person image to a target pose. This task requires spatial manipulations of source data. However, Convolutional Neural Networks are limited by lacking the ability to spatially transform the inputs. In this paper, we propose a differentiable global-flow local-attention framework to reassemble the inputs at the feature level. Specifically, our model first calculates the global correlations between sources and targets to predict flow fields. Then, the flowed local patch pairs are extracted from the feature maps to calculate the local attention coefficients. Finally, we warp the source features using a content-aware sampling method with the obtained local attention coefficients. The results of both subjective and objective experiments demonstrate the superiority of our model. Besides, additional results in video animation and view synthesis show that our model is applicable to other tasks requiring spatial transformation. Our source code is available at this https URL.

Includes members:
  Member "multi channel attention selection gans for guided image to image translation":
    also connected through other hyperedges:
      (none)
  Member "progressive pose attention transfer for person image generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "unsupervised person image generation with semantic parsing transformation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to transfer texture from clothing images to 3d humans")
  Member "liquid warping gan a unified framework for human motion imitation appearance transfer and novel view synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")
  Member "linearized multi sampling for differentiable image transformation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "synsin end to end view synthesis from a single image")
  Member "dense intrinsic appearance flow for human pose transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")
  Member "structureflow image inpainting via structure aware appearance flow":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "domain embedded multi model generative adversarial networks for image based face inpainting")
  Member "cycle in cycle generative adversarial networks for keypoint guided image generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi channel attention selection gans for guided image to image translation")

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)