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: "hairnet single view hair reconstruction using convolutional neural networks"
  Abstract: We introduce a deep learning-based method to generate full 3D hair geometry from an unconstrained image. Our method can recover local strand details and has real-time performance. State-of-the-art hair modeling techniques rely on large hairstyle collections for nearest neighbor retrieval and then perform ad-hoc refinement. Our deep learning approach, in contrast, is highly efficient in storage and can run 1000 times faster while generating hair with 30K strands. The convolutional neural network takes the 2D orientation field of a hair image as input and generates strand features that are evenly distributed on the parameterized 2D scalp. We introduce a collision loss to synthesize more plausible hairstyles, and the visibility of each strand is also used as a weight term to improve the reconstruction accuracy. The encoder-decoder architecture of our network naturally provides a compact and continuous representation for hairstyles, which allows us to interpolate naturally between hairstyles. We use a large set of rendered synthetic hair models to train our network. Our method scales to real images because an intermediate 2D orientation field, automatically calculated from the real image, factors out the difference between synthetic and real hairs. We demonstrate the effectiveness and robustness of our method on a wide range of challenging real Internet pictures and show reconstructed hair sequences from videos.

Connected through hyperedge HE-1 (induced by source paper "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "geometry aware network for non rigid shape prediction from a single view"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "unsupervised training for 3d morphable model regression"
    - "siclope silhouette based clothed people"
    - "single image svbrdf capture with a rendering aware deep network"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"
    - "nonlinear 3d face morphable model"

Connected through hyperedge HE-2 (induced by source paper "two phase hair image synthesis by self enhancing generative model"):
  with members:
    - "gif2video color dequantization and temporal interpolation of gif images"
    - "faceshop deep sketch based face image editing"
    - "learning dual convolutional neural networks for low level vision"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "residual dense network for image super resolution"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "mganet a robust model for quality enhancement of compressed video"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-3 (induced by source paper "deep mesh reconstruction from single rgb images via topology modification networks"):
  with members:
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"
    - "deep level sets implicit surface representations for 3d shape inference"
    - "learning category specific mesh reconstruction from image collections"
    - "bodynet volumetric inference of 3d human body shapes"
    - "learning implicit fields for generative shape modeling"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "siclope silhouette based clothed people"
    - "unsupervised learning of shape and pose with differentiable point clouds"

Connected through hyperedge HE-4 (induced by source paper "semi supervised skin detection by network with mutual guidance"):
  with members:
    - "gif2video color dequantization and temporal interpolation of gif images"
    - "frame recurrent video inpainting by robust optical flow inference"
    - "two phase hair image synthesis by self enhancing generative model"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "mganet a robust model for quality enhancement of compressed video"

Connected through hyperedge HE-5 (induced by source paper "deepsketchhair deep sketch based 3d hair modeling"):
  with members:
    - "v2v posenet voxel to voxel prediction network for accurate 3d hand and human pose estimation from a single depth map"
    - "deepsketch2face a deep learning based sketching system for 3d face and caricature modeling"
    - "3d shape reconstruction from sketches via multi view convolutional networks"
    - "improved training of wasserstein gans"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "photographic image synthesis with cascaded refinement networks"
    - "3d sketching using multi view deep volumetric prediction"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "hair segmentation on time of flight rgbd images"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "video to fully automatic 3d hair model"
    - "xception deep learning with depthwise separable convolutions"
    - "pyramid scene parsing network"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "imagenet large scale visual recognition challenge"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-7 (induced by source paper "hair gans recovering 3d hair structure from a single image"):
  with members:
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "generative adversarial text to image synthesis"
    - "adam a method for stochastic optimization"

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.GR(Graphics)