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: "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
  Abstract: We introduce, TextureNet, a neural network architecture designed to extract features from high-resolution signals associated with 3D surface meshes (e.g., color texture maps). The key idea is to utilize a 4-rotational symmetric (4-RoSy) field to define a domain for convolution on a surface. Though 4-RoSy fields have several properties favorable for convolution on surfaces (low distortion, few singularities, consistent parameterization, etc.), orientations are ambiguous up to 4-fold rotation at any sample point. So, we introduce a new convolutional operator invariant to the 4-RoSy ambiguity and use it in a network to extract features from high-resolution signals on geodesic neighborhoods of a surface. In comparison to alternatives, such as PointNet based methods which lack a notion of orientation, the coherent structure given by these neighborhoods results in significantly stronger features. As an example application, we demonstrate the benefits of our architecture for 3D semantic segmentation of textured 3D meshes. The results show that our method outperforms all existing methods on the basis of mean IoU by a significant margin in both geometry-only (6.4%) and RGB+Geometry (6.9-8.2%) settings.

Connected through hyperedge HE-1 (induced by source paper "fpconv learning local flattening for point convolution"):
  with members:
    - "kpconv flexible and deformable convolution for point clouds"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "hierarchical point edge interaction network for point cloud semantic segmentation"
    - "deepgcns can gcns go as deep as cnns"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "a lidar point cloud generator from a virtual world to autonomous driving"
    - "pointconv deep convolutional networks on 3d point clouds"

Connected through hyperedge HE-2 (induced by source paper "continuous geodesic convolutions for learning on 3d shapes"):
  with members:
    - "3d local features for direct pairwise registration"
    - "kpconv flexible and deformable convolution for point clouds"
    - "meshcnn a network with an edge"
    - "quaternion equivariant capsule networks for 3d point clouds"
    - "self supervised learning of dense shape correspondence"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "point convolutional neural networks by extension operators"
    - "multi directional geodesic neural networks via equivariant convolution"

Connected through hyperedge HE-3 (induced by source paper "sceneencoder scene aware semantic segmentation of point clouds with a learnable scene descriptor"):
  with members:
    - "octree guided cnn with spherical kernels for 3d point clouds"
    - "hierarchical point edge interaction network for point cloud semantic segmentation"
    - "shellnet efficient point cloud convolutional neural networks using concentric shells statistics"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "so net self organizing network for point cloud analysis"
    - "pointconv deep convolutional networks on 3d point clouds"

Connected through hyperedge HE-4 (induced by source paper "ddsl deep differentiable simplex layer for learning geometric signals"):
  with members:
    - "spherical cnns on unstructured grids"
    - "convolutional neural networks on non uniform geometrical signals using euclidean spectral transformation"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "learning category specific mesh reconstruction from image collections"
    - "unsupervised training for 3d morphable model regression"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-5 (induced by source paper "latticenet fast point cloud segmentation using permutohedral lattices"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "hplflownet hierarchical permutohedral lattice flownet for scene flow estimation on large scale point clouds"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "group normalization"
    - "splatnet sparse lattice networks for point cloud processing"
    - "real time progressive 3d semantic segmentation for indoor scene"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"

Connected through hyperedge HE-6 (induced by source paper "uprightnet geometry aware camera orientation estimation from single images"):
  with members:
    - "learning the depths of moving people by watching frozen people"
    - "framenet learning local canonical frames of 3d surfaces from a single rgb image"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "groundnet segmentation aware monocular ground plane estimation with geometric consistency"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "deep depth completion of a single rgb d image"

Connected through hyperedge HE-7 (induced by source paper "framenet learning local canonical frames of 3d surfaces from a single rgb image"):
  with members:
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth completion of a single rgb d image"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "tangent convolutions for dense prediction in 3d"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "lift learned invariant feature transform"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "panopticfusion online volumetric semantic mapping at the level of stuff and things"):
  with members:
    - "masc multi scale affinity with sparse convolution for 3d instance segmentation"
    - "fusion volumetric object level slam"
    - "3d sis 3d semantic instance segmentation of rgb d scans"
    - "splatnet sparse lattice networks for point cloud processing"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"
    - "tangent convolutions for dense prediction in 3d"
    - "mid fusion octree based object level multi instance dynamic slam"

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)