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: "view extrapolation of human body from a single image"
  Abstract: We study how to synthesize novel views of human body from a single image. Though recent deep learning based methods work well for rigid objects, they often fail on objects with large articulation, like human bodies. The core step of existing methods is to fit a map from the observable views to novel views by CNNs; however, the rich articulation modes of human body make it rather challenging for CNNs to memorize and interpolate the data well. To address the problem, we propose a novel deep learning based pipeline that explicitly estimates and leverages the geometry of the underlying human body. Our new pipeline is a composition of a shape estimation network and an image generation network, and at the interface a perspective transformation is applied to generate a forward flow for pixel value transportation. Our design is able to factor out the space of data variation and makes learning at each step much easier. Empirically, we show that the performance for pose-varying objects can be improved dramatically. Our method can also be applied on real data captured by 3D sensors, and the flow generated by our methods can be used for generating high quality results in higher resolution.

Connected through hyperedge HE-1 (induced by source paper "monocular neural image based rendering with continuous view control"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "transformable bottleneck networks"
    - "deepview view synthesis with learned gradient descent"
    - "local light field fusion practical view synthesis with prescriptive sampling guidelines"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "pushing the boundaries of view extrapolation with multiplane images"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "visual object networks image generation with disentangled 3d representation"

Connected through hyperedge HE-2 (induced by source paper "a neural rendering framework for free viewpoint relighting"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "visual representation learning with 3d view contrastive inverse graphics networks"
    - "transformable bottleneck networks"
    - "differentiable surface splatting for point based geometry processing"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "monet unsupervised scene decomposition and representation"
    - "scene representation networks continuous 3d structure aware neural scene representations"
    - "neural point based graphics"

Connected through hyperedge HE-3 (induced by source paper "neural rendering and reenactment of human actor videos"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "a variational u net for conditional appearance and shape generation"
    - "pose guided person image 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"

Connected through hyperedge HE-4 (induced by source paper "liquid warping gan a unified framework for human motion imitation appearance transfer and novel view synthesis"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "video to video synthesis"
    - "soft gated warping gan for pose guided person image synthesis"
    - "synthesizing images of humans in unseen poses"
    - "a variational u net for conditional appearance and shape generation"
    - "dense pose transfer"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "ganimation anatomically aware facial animation from a single image"

Connected through hyperedge HE-5 (induced by source paper "neural rendering and reenactment of human actor videos"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "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"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "ganerated hands for real time 3d hand tracking from monocular rgb"

Connected through hyperedge HE-6 (induced by source paper "detailed human shape estimation from a single image by hierarchical mesh deformation"):
  with members:
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "densereg fully convolutional dense shape regression in the wild"
    - "video based reconstruction of 3d people models"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "deep textured 3d reconstruction of human bodies"
    - "densepose dense human pose estimation in the wild"
    - "detailed surface geometry and albedo recovery from rgb d video under natural illumination"

Connected through hyperedge HE-7 (induced by source paper "deepvoxels learning persistent 3d feature embeddings"):
  with members:
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "layer structured 3d scene inference via view synthesis"
    - "learning free form deformations for 3d object reconstruction"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"
    - "everybody dance now"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "learning efficient point cloud generation for dense 3d object reconstruction"

Connected through hyperedge HE-8 (induced by source paper "siclope silhouette based clothed people"):
  with members:
    - "gif2video color dequantization and temporal interpolation of gif images"
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "synthesizing images of humans in unseen poses"
    - "bodynet volumetric inference of 3d human body shapes"
    - "video based reconstruction of 3d people models"
    - "photo wake up 3d character animation from a single photo"
    - "3d human pose estimation in the wild by adversarial learning"
    - "a variational u net for conditional appearance and shape generation"

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)