human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "towards multi pose guided virtual try on network"
  Abstract: Virtual try-on system under arbitrary human poses has huge application potential, yet raises quite a lot of challenges, e.g. self-occlusions, heavy misalignment among diverse poses, and diverse clothes textures. Existing methods aim at fitting new clothes into a person can only transfer clothes on the fixed human pose, but still show unsatisfactory performances which often fail to preserve the identity, lose the texture details, and decrease the diversity of poses. In this paper, we make the first attempt towards multi-pose guided virtual try-on system, which enables transfer clothes on a person image under diverse poses. Given an input person image, a desired clothes image, and a desired pose, the proposed Multi-pose Guided Virtual Try-on Network (MG-VTON) can generate a new person image after fitting the desired clothes into the input image and manipulating human poses. Our MG-VTON is constructed in three stages: 1) a desired human parsing map of the target image is synthesized to match both the desired pose and the desired clothes shape; 2) a deep Warping Generative Adversarial Network (Warp-GAN) warps the desired clothes appearance into the synthesized human parsing map and alleviates the misalignment problem between the input human pose and desired human pose; 3) a refinement render utilizing multi-pose composition masks recovers the texture details of clothes and removes some artifacts. Extensive experiments on well-known datasets and our newly collected largest virtual try-on benchmark demonstrate that our MG-VTON significantly outperforms all state-of-the-art methods both qualitatively and quantitatively with promising multi-pose virtual try-on performances.

Connected through hyperedge HE-1 (induced by source paper "end to end learning of geometric deformations of feature maps for virtual try on"):
  with members:
    - "unsupervised part based disentangling of object shape and appearance"
    - "synthesizing images of humans in unseen poses"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "the relativistic discriminator a key element missing from standard gan"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "toward characteristic preserving image based virtual try on network"
    - "a variational u net for conditional appearance and shape generation"
    - "spatial fusion gan for image synthesis"

Connected through hyperedge HE-2 (induced by source paper "fashion editing with adversarial parsing learning"):
  with members:
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "foreground aware image inpainting"
    - "semantic image synthesis with spatially adaptive normalization"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "faceshop deep sketch based face image editing"
    - "free form image inpainting with gated convolution"
    - "semi parametric image inpainting"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-3 (induced by source paper "poly gan multi conditioned gan for fashion synthesis"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "soft gated warping gan for pose guided person image synthesis"
    - "unet a nested u net architecture for medical image segmentation"
    - "a style based generator architecture for generative adversarial networks"
    - "viton an image based virtual try on network"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "progressive growing of gans for improved quality stability and variation"
    - "convolutional neural network architecture for geometric matching"

Question: Please tell me which class the center node 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)