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 "soft gated warping gan for pose guided person image synthesis"):
  Abstract: Despite remarkable advances in image synthesis research, existing works often fail in manipulating images under the context of large geometric transformations. Synthesizing person images conditioned on arbitrary poses is one of the most representative examples where the generation quality largely relies on the capability of identifying and modeling arbitrary transformations on different body parts. Current generative models are often built on local convolutions and overlook the key challenges (e.g. heavy occlusions, different views or dramatic appearance changes) when distinct geometric changes happen for each part, caused by arbitrary pose manipulations. This paper aims to resolve these challenges induced by geometric variability and spatial displacements via a new Soft-Gated Warping Generative Adversarial Network (Warping-GAN), which is composed of two stages: 1) it first synthesizes a target part segmentation map given a target pose, which depicts the region-level spatial layouts for guiding image synthesis with higher-level structure constraints; 2) the Warping-GAN equipped with a soft-gated warping-block learns feature-level mapping to render textures from the original image into the generated segmentation map. Warping-GAN is capable of controlling different transformation degrees given distinct target poses. Moreover, the proposed warping-block is light-weight and flexible enough to be injected into any networks. Human perceptual studies and quantitative evaluations demonstrate the superiority of our Warping-GAN that significantly outperforms all existing methods on two large datasets.

Includes members:
  Member "synthesizing images of humans in unseen poses":
    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 "unsupervised person image synthesis in arbitrary poses":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video 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 "a variational u net for conditional appearance and shape generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "pose robust face recognition via deep residual equivariant mapping":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distribution distillation loss generic approach for improving face recognition from hard samples")
  Member "viton an image based virtual try on network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to transfer texture from clothing images to 3d humans")
  Member "dual motion gan for future flow embedded video prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "look into person self supervised structure sensitive learning and a new benchmark for human parsing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hierarchical human parsing with typed part relation reasoning")

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)