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: "deformnet free form deformation network for 3d shape reconstruction from a single image"
  Abstract: 3D reconstruction from a single image is a key problem in multiple applications ranging from robotic manipulation to augmented reality. Prior methods have tackled this problem through generative models which predict 3D reconstructions as voxels or point clouds. However, these methods can be computationally expensive and miss fine details. We introduce a new differentiable layer for 3D data deformation and use it in DeformNet to learn a model for 3D reconstruction-through-deformation. DeformNet takes an image input, searches the nearest shape template from a database, and deforms the template to match the query image. We evaluate our approach on the ShapeNet dataset and show that - (a) the Free-Form Deformation layer is a powerful new building block for Deep Learning models that manipulate 3D data (b) DeformNet uses this FFD layer combined with shape retrieval for smooth and detail-preserving 3D reconstruction of qualitatively plausible point clouds with respect to a single query image (c) compared to other state-of-the-art 3D reconstruction methods, DeformNet quantitatively matches or outperforms their benchmarks by significant margins. For more information, visit: this https URL .

Connected through hyperedge HE-1 (induced by source paper "total3dunderstanding joint layout object pose and mesh reconstruction for indoor scenes from a single image"):
  with members:
    - "3d scene reconstruction with multi layer depth and epipolar transformers"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning elementary structures for 3d shape generation and matching"
    - "holistic scene understanding single view 3d holistic scene parsing and human pose estimation with human object interaction and physical commonsense"
    - "learning to infer and execute 3d shape programs"
    - "a skeleton bridged deep learning approach for generating meshes of complex topologies from single rgb images"
    - "few shot generalization for single image 3d reconstruction via priors"
    - "deep level sets implicit surface representations for 3d shape inference"

Connected through hyperedge HE-2 (induced by source paper "dense 3d object reconstruction from a single depth view"):
  with members:
    - "defo net learning body deformation using generative adversarial networks"
    - "mcgan mean and covariance feature matching gan"
    - "3d prnn generating shape primitives with recurrent neural networks"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "octnetfusion learning depth fusion from data"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "progressive growing of gans for improved quality stability and variation"
    - "end to end 3d face reconstruction with deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "conditional single view shape generation for multi view stereo reconstruction"):
  with members:
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "recurrent transformer networks for semantic correspondence"
    - "so net self organizing network for point cloud analysis"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "pixels voxels and views a study of shape representations for single view 3d object shape prediction"
    - "learning to reconstruct shapes from unseen classes"
    - "pix3d dataset and methods for single image 3d shape modeling"

Connected through hyperedge HE-4 (induced by source paper "pointflow 3d point cloud generation with continuous normalizing flows"):
  with members:
    - "videoflow a flow based generative model for video"
    - "pointwise an unsupervised point wise feature learning network"
    - "waveglow a flow based generative network for speech synthesis"
    - "multiresolution tree networks for 3d point cloud processing"
    - "deep sets"
    - "adversarial autoencoders for compact representations of 3d point clouds"
    - "an empirical study on evaluation metrics of generative adversarial networks"
    - "point cloud gan"

Connected through hyperedge HE-5 (induced by source paper "self supervised learning of 3d objects from natural images"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "what do single view 3d reconstruction networks learn"
    - "learning to infer implicit surfaces without 3d supervision"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "learning view priors for single view 3d reconstruction"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "learning category specific mesh reconstruction from image collections"

Connected through hyperedge HE-6 (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"
    - "geometry aware network for non rigid shape prediction from a single view"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "pixel2mesh multi view 3d mesh generation via deformation"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "what do single view 3d reconstruction networks learn"
    - "dpsnet end to end deep plane sweep stereo"
    - "matryoshka networks predicting 3d geometry via nested shape layers"
    - "learning category specific mesh reconstruction from image collections"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "deepmvs learning multi view stereopsis"
    - "learning implicit fields for generative shape modeling"

Connected through hyperedge HE-8 (induced by source paper "sdfdiff differentiable rendering of signed distance fields for 3d shape optimization"):
  with members:
    - "differentiable surface splatting for point based geometry processing"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "deep level sets implicit surface representations for 3d shape inference"
    - "visual object networks image generation with disentangled 3d representation"
    - "y 2seq2seq cross modal representation learning for 3d shape and text by joint reconstruction and prediction of view and word sequences"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"

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)