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: "robust watertight manifold surface generation method for shapenet models"
  Abstract: In this paper, we describe a robust algorithm for 2-Manifold generation of various kinds of ShapeNet Models. The input of our pipeline is a triangle mesh, with a set of vertices and triangular faces. The output of our pipeline is a 2-Manifold with vertices roughly uniformly distributed on the geometry surface. Our algorithm uses an octree to represent the original mesh, and construct the surface by isosurface extraction. Finally, we project the vertices to the original mesh to achieve high precision. As a result, our method can be adopted efficiently to all ShapeNet models with the guarantee of correct 2-Manifold topology.

Connected through hyperedge HE-1 (induced by source paper "instant recovery of shape from spectrum via latent space connections"):
  with members:
    - "operatornet recovering 3d shapes from difference operators"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "geometric disentanglement for generative latent shape models"
    - "correspondence free region localization for partial shape similarity via hamiltonian spectrum alignment"
    - "sdm net deep generative network for structured deformable mesh"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "unsupervised deep learning for structured shape matching"
    - "learning implicit fields for generative shape modeling"

Connected through hyperedge HE-2 (induced by source paper "adversarial texture optimization from rgb d scans"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "neural point based graphics"
    - "framenet learning local canonical frames of 3d surfaces from a single rgb image"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "perceptual losses for real time style transfer and super resolution"
    - "image to image translation with conditional adversarial networks"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"

Connected through hyperedge HE-3 (induced by source paper "deep sketch based modeling of man made shapes"):
  with members:
    - "deepspline data driven reconstruction of parametric curves and surfaces"
    - "learning shape templates with structured implicit functions"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to infer and execute 3d shape programs"
    - "3dn 3d deformation network"
    - "deep parametric shape predictions using distance fields"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"

Connected through hyperedge HE-4 (induced by source paper "sdm net deep generative network for structured deformable mesh"):
  with members:
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "meshcnn a network with an edge"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "vv net voxel vae net with group convolutions for point cloud segmentation"
    - "p2p net bidirectional point displacement net for shape transform"
    - "multi directional geodesic neural networks via equivariant convolution"

Connected through hyperedge HE-5 (induced by source paper "laplaciannet learning on 3d meshes with laplacian encoding and pooling"):
  with members:
    - "adaptive graph convolutional neural networks"
    - "so net self organizing network for point cloud analysis"
    - "3d mesh segmentation via multi branch 1d convolutional neural networks"
    - "3d shape segmentation via shape fully convolutional networks"
    - "feastnet feature steered graph convolutions for 3d shape analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "geometric deep learning going beyond euclidean data"
    - "3d shape segmentation with projective convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "prs net planar reflective symmetry detection net for 3d models"):
  with members:
    - "abc a big cad model dataset for geometric deep learning"
    - "learning shape abstractions by assembling volumetric primitives"
    - "learning a predictable and generative vector representation for objects"
    - "thingi10k a dataset of 10 000 3d printing models"
    - "octnet learning deep 3d representations at high resolutions"
    - "shapenet an information rich 3d model repository"
    - "adam a method for stochastic optimization"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-7 (induced by source paper "deep level sets implicit surface representations for 3d shape inference"):
  with members:
    - "matryoshka networks predicting 3d geometry via nested shape layers"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning a predictable and generative vector representation for objects"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "unsupervised learning of 3d structure from images"
    - "perspective transformer nets learning single view 3d object reconstruction without 3d supervision"
    - "shapenet an information rich 3d model repository"
    - "learning to generate chairs with convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"):
  with members:
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "voxsegnet volumetric cnns for semantic part segmentation of 3d shapes"
    - "virtualhome simulating household activities via programs"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "chalet cornell house agent learning environment"
    - "splatnet sparse lattice networks for point cloud processing"
    - "dynamic graph cnn for learning on point clouds"

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.CG(Computational Geometry)