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: "geometry aware network for non rigid shape prediction from a single view"
  Abstract: We propose a method for predicting the 3D shape of a deformable surface from a single view. By contrast with previous approaches, we do not need a pre-registered template of the surface, and our method is robust to the lack of texture and partial occlusions. At the core of our approach is a {\it geometry-aware} deep architecture that tackles the problem as usually done in analytic solutions: first perform 2D detection of the mesh and then estimate a 3D shape that is geometrically consistent with the image. We train this architecture in an end-to-end manner using a large dataset of synthetic renderings of shapes under different levels of deformation, material properties, textures and lighting conditions. We evaluate our approach on a test split of this dataset and available real benchmarks, consistently improving state-of-the-art solutions with a significantly lower computational time.

Connected through hyperedge HE-1 (induced by source paper "3dpeople modeling the geometry of dressed humans"):
  with members:
    - "wav2pix speech conditioned face generation using generative adversarial networks"
    - "bodynet volumetric inference of 3d human body shapes"
    - "unsupervised person image synthesis in arbitrary poses"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "3d human pose estimation in the wild by adversarial learning"
    - "siclope silhouette based clothed people"
    - "talking face generation by conditional recurrent adversarial network"

Connected through hyperedge HE-2 (induced by source paper "c flow conditional generative flow models for images and 3d point clouds"):
  with members:
    - "revealing scenes by inverting structure from motion reconstructions"
    - "conditional adversarial generative flow for controllable image synthesis"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "neural point based graphics"
    - "fast video object segmentation with spatio temporal gans"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"

Connected through hyperedge HE-3 (induced by source paper "ismo gan adversarial learning for monocular non rigid 3d reconstruction"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "generative image inpainting with contextual attention"
    - "octnetfusion learning depth fusion from data"
    - "progressive growing of gans for improved quality stability and variation"
    - "feastnet feature steered graph convolutions for 3d shape analysis"
    - "better together joint reasoning for non rigid 3d reconstruction with specularities and shading"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "hairnet single view hair reconstruction using convolutional neural networks"
    - "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-5 (induced by source paper "self supervised learning of state estimation for manipulating deformable linear objects"):
  with members:
    - "learning robotic manipulation through visual planning and acting"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "propagation networks for model based control under partial observation"
    - "unsupervised intuitive physics from visual observations"
    - "self supervised visual planning with temporal skip connections"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "combining learned and analytical models for predicting action effects"

Connected through hyperedge HE-6 (induced by source paper "shape reconstruction by learning differentiable surface representations"):
  with members:
    - "learning elementary structures for 3d shape generation and matching"
    - "deep learning parametrization for b spline curve approximation"
    - "garnet a two stream network for fast and accurate 3d cloth draping"
    - "pixels voxels and views a study of shape representations for single view 3d object shape prediction"
    - "deep geometric prior for surface reconstruction"
    - "foldingnet point cloud auto encoder via deep grid deformation"
    - "surfnet generating 3d shape surfaces using deep residual networks"
    - "a point set generation network for 3d object reconstruction from a single image"

Connected through hyperedge HE-7 (induced by source paper "dispvoxnets non rigid point set alignment with supervised learning proxies"):
  with members:
    - "demea deep mesh autoencoders for non rigidly deforming objects"
    - "alignet partial shape agnostic alignment via unsupervised learning"
    - "3d physnet learning the intuitive physics of non rigid object deformations"
    - "learning free form deformations for 3d object reconstruction"
    - "deformnet free form deformation network for 3d shape reconstruction from a single image"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "octnet learning deep 3d representations at high resolutions"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "intrinsic dynamic shape prior for fast sequential and dense non rigid structure from motion with detection of temporally disjoint rigidity"):
  with members:
    - "ismo gan adversarial learning for monocular non rigid 3d reconstruction"
    - "structure from articulated motion an accurate and stable monocular 3d reconstruction approach without training data"
    - "a benchmark and evaluation of non rigid structure from motion"
    - "structure from recurrent motion from rigidity to recurrency"
    - "accurate 3d reconstruction of dynamic scenes from monocular image sequences with severe occlusions"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "scalable dense monocular surface reconstruction"

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)