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: "dense intrinsic appearance flow for human pose transfer"
  Abstract: We present a novel approach for the task of human pose transfer, which aims at synthesizing a new image of a person from an input image of that person and a target pose. We address the issues of limited correspondences identified between keypoints only and invisible pixels due to self-occlusion. Unlike existing methods, we propose to estimate dense and intrinsic 3D appearance flow to better guide the transfer of pixels between poses. In particular, we wish to generate the 3D flow from just the reference and target poses. Training a network for this purpose is non-trivial, especially when the annotations for 3D appearance flow are scarce by nature. We address this problem through a flow synthesis stage. This is achieved by fitting a 3D model to the given pose pair and project them back to the 2D plane to compute the dense appearance flow for training. The synthesized ground-truths are then used to train a feedforward network for efficient mapping from the input and target skeleton poses to the 3D appearance flow. With the appearance flow, we perform feature warping on the input image and generate a photorealistic image of the target pose. Extensive results on DeepFashion and Market-1501 datasets demonstrate the effectiveness of our approach over existing methods. Our code is available at this http URL

Connected through hyperedge HE-1 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "textured neural avatars"
    - "neural rendering and reenactment of human actor videos"
    - "liquid warping gan a unified framework for human motion imitation appearance transfer and novel view synthesis"
    - "video to video synthesis"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "synthesizing images of humans in unseen poses"
    - "learning to estimate 3d human pose and shape from a single color image"

Connected through hyperedge HE-2 (induced by source paper "deep image spatial transformation for person image generation"):
  with members:
    - "multi channel attention selection gans for guided image to image translation"
    - "progressive pose attention transfer for person image generation"
    - "unsupervised person image generation with semantic parsing transformation"
    - "liquid warping gan a unified framework for human motion imitation appearance transfer and novel view synthesis"
    - "linearized multi sampling for differentiable image transformation"
    - "structureflow image inpainting via structure aware appearance flow"
    - "cycle in cycle generative adversarial networks for keypoint guided image generation"
    - "local relation networks for image recognition"

Connected through hyperedge HE-3 (induced by source paper "unsupervised pose flow learning for pose guided synthesis"):
  with members:
    - "soft gated warping gan for pose guided person image synthesis"
    - "toward characteristic preserving image based virtual try on network"
    - "spectral normalization for generative adversarial networks"
    - "a variational u net for conditional appearance and shape generation"
    - "dense pose transfer"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "m2e try on net fashion from model to everyone"
    - "densepose dense human pose estimation in the wild"

Connected through hyperedge HE-4 (induced by source paper "vid2game controllable characters extracted from real world videos"):
  with members:
    - "bottom up object detection by grouping extreme and center points"
    - "video to video synthesis"
    - "pose guided human video generation"
    - "cornernet detecting objects as paired keypoints"
    - "synthesizing images of humans in unseen poses"
    - "unsupervised person image synthesis in arbitrary poses"
    - "learning 3d human dynamics from video"
    - "sfv reinforcement learning of physical skills from videos"

Connected through hyperedge HE-5 (induced by source paper "human synthesis and scene compositing"):
  with members:
    - "soft gated warping gan for pose guided person image synthesis"
    - "learning to segment via cut and paste"
    - "synthesizing images of humans in unseen poses"
    - "unsupervised person image synthesis in arbitrary poses"
    - "instance level human parsing via part grouping network"
    - "modeling visual context is key to augmenting object detection datasets"
    - "a variational u net for conditional appearance and shape generation"
    - "dense pose transfer"

Connected through hyperedge HE-6 (induced by source paper "one shot face reenactment"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "facelet bank for fast portrait manipulation"
    - "synthesizing images of humans in unseen poses"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "a variational u net for conditional appearance and shape generation"
    - "instance level facial attributes transfer with geometry aware flow"
    - "reenactgan learning to reenact faces via boundary transfer"

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)