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: "pose guided person image generation"
  Abstract: This paper proposes the novel Pose Guided Person Generation Network (PG$^2$) that allows to synthesize person images in arbitrary poses, based on an image of that person and a novel pose. Our generation framework PG$^2$ utilizes the pose information explicitly and consists of two key stages: pose integration and image refinement. In the first stage the condition image and the target pose are fed into a U-Net-like network to generate an initial but coarse image of the person with the target pose. The second stage then refines the initial and blurry result by training a U-Net-like generator in an adversarial way. Extensive experimental results on both 128$\times$64 re-identification images and 256$\times$256 fashion photos show that our model generates high-quality person images with convincing details.

Connected through hyperedge HE-1 (induced by source paper "chained representation cycling learning to estimate 3d human pose and shape by cycling between representations"):
  with members:
    - "unsupervised part based disentangling of object shape and appearance"
    - "unsupervised learning of object structure and dynamics from videos"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "ordinal depth supervision for 3d human pose estimation"
    - "synthesizing images of humans in unseen poses"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "bodynet volumetric inference of 3d human body shapes"

Connected through hyperedge HE-2 (induced by source paper "a robust pose transformational gan for pose guided person image synthesis"):
  with members:
    - "soft gated warping gan for pose guided person image synthesis"
    - "synthesizing images of humans in unseen poses"
    - "stellar cluster detection using gmm with deep variational autoencoder"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "random erasing data augmentation"
    - "skeleton aided articulated motion generation"
    - "pose normalized image generation for person re identification"
    - "disentangled person image generation"

Connected through hyperedge HE-3 (induced by source paper "network of steel neural font style transfer from heavy metal to corporate logos"):
  with members:
    - "controllable artistic text style transfer via shape matching gan"
    - "glyphgan style consistent font generation based on generative adversarial networks"
    - "contained neural style transfer for decorated logo generation"
    - "multi style generative network for real time transfer"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "multi content gan for few shot font style transfer"
    - "image to image translation with conditional adversarial networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "reshapegan object reshaping by providing a single reference image"):
  with members:
    - "representation learning by rotating your faces"
    - "semantic image synthesis with spatially adaptive normalization"
    - "pose guided human video generation"
    - "the gan landscape losses architectures regularization and normalization"
    - "pros and cons of gan evaluation measures"
    - "multimodal unsupervised image to image translation"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "generative adversarial networks an overview"

Connected through hyperedge HE-5 (induced by source paper "cycle in cycle generative adversarial networks for keypoint guided image generation"):
  with members:
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "gazecorrection self guided eye manipulation in the wild using self supervised generative adversarial networks"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cascade attention guided residue learning gan for cross modal translation"
    - "attention guided generative adversarial networks for unsupervised image to image translation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "video to video synthesis"
    - "cross view image synthesis using conditional gans"

Connected through hyperedge HE-6 (induced by source paper "dual generator generative adversarial networks for multi domain image to image translation"):
  with members:
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "every smile is unique landmark guided diverse smile generation"
    - "dual discriminator generative adversarial nets"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "generative face completion"
    - "combogan unrestrained scalability for image domain translation"

Connected through hyperedge HE-7 (induced by source paper "gesture to gesture translation in the wild via category independent conditional maps"):
  with members:
    - "3d guided fine grained face manipulation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "pros and cons of gan evaluation measures"
    - "triple consistency loss for pairing distributions in gan based face synthesis"
    - "instagan instance aware image to image translation"
    - "ganimation anatomically aware facial animation from a single image"
    - "animating arbitrary objects via deep motion transfer"

Connected through hyperedge HE-8 (induced by source paper "neural rendering and reenactment of human actor videos"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "view extrapolation of human body from a single image"
    - "a variational u net for conditional appearance and shape generation"
    - "progressive growing of gans for improved quality stability and variation"
    - "carla an open urban driving simulator"
    - "a generative model of people in clothing"
    - "deformable gans for pose based human image generation"
    - "unsupervised image to image translation networks"

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)