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: "learning 3d shapes as multi layered height maps using 2d convolutional networks"
  Abstract: We present a novel global representation of 3D shapes, suitable for the application of 2D CNNs. We represent 3D shapes as multi-layered height-maps (MLH) where at each grid location, we store multiple instances of height maps, thereby representing 3D shape detail that is hidden behind several layers of occlusion. We provide a novel view merging method for combining view dependent information (Eg. MLH descriptors) from multiple views. Because of the ability of using 2D CNNs, our method is highly memory efficient in terms of input resolution compared to the voxel based input. Together with MLH descriptors and our multi view merging, we achieve the state-of-the-art result in classification on ModelNet dataset.

Connected through hyperedge HE-1 (induced by source paper "meshcnn a network with an edge"):
  with members:
    - "spherical cnns on unstructured grids"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "alignet partial shape agnostic alignment via unsupervised learning"
    - "dual primal graph convolutional networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "point convolutional neural networks by extension operators"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-2 (induced by source paper "multiview aggregation for learning category specific shape reconstruction"):
  with members:
    - "equivariant multi view networks"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "learning category specific mesh reconstruction from image collections"
    - "deepmvs learning multi view stereopsis"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "deep sets"

Connected through hyperedge HE-3 (induced by source paper "structured 2d representation of 3d data for shape processing"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "pixor real time 3d object detection from point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "mask r cnn"
    - "3d shape segmentation with projective convolutional networks"
    - "orientation boosted voxel nets for 3d object recognition"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"

Connected through hyperedge HE-4 (induced by source paper "efficient learning on point clouds with basis point sets"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "3d coded 3d correspondences by deep deformation"
    - "deep sets"
    - "farm functional automatic registration method for 3d human bodies"
    - "tangent convolutions for dense prediction in 3d"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "octnetfusion learning depth fusion from data"

Connected through hyperedge HE-5 (induced by source paper "3d object recognition with ensemble learning a study of point cloud based deep learning models"):
  with members:
    - "a deeper look at 3d shape classifiers"
    - "deep sets"
    - "so net self organizing network for point cloud analysis"
    - "dynamic graph cnn for learning on point clouds"
    - "scannet richly annotated 3d reconstructions of indoor scenes"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "mining point cloud local structures by kernel correlation and graph pooling"
    - "efficient processing of deep neural networks a tutorial and survey"

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)