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: "efficient 2d and 3d facade segmentation using auto context"
  Abstract: This paper introduces a fast and efficient segmentation technique for 2D images and 3D point clouds of building facades. Facades of buildings are highly structured and consequently most methods that have been proposed for this problem aim to make use of this strong prior information. Contrary to most prior work, we are describing a system that is almost domain independent and consists of standard segmentation methods. We train a sequence of boosted decision trees using auto-context features. This is learned using stacked generalization. We find that this technique performs better, or comparable with all previous published methods and present empirical results on all available 2D and 3D facade benchmark datasets. The proposed method is simple to implement, easy to extend, and very efficient at test-time inference.

Connected through hyperedge HE-1 (induced by source paper "spherical kernel for efficient graph convolution on 3d point clouds"):
  with members:
    - "octree guided cnn with spherical kernels for 3d point clouds"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "so net self organizing network for point cloud analysis"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "dense 3d face correspondence"
    - "learning to sample"

Connected through hyperedge HE-2 (induced by source paper "octree guided cnn with spherical kernels for 3d point clouds"):
  with members:
    - "so net self organizing network for point cloud analysis"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "mining point cloud local structures by kernel correlation and graph pooling"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-3 (induced by source paper "splatnet sparse lattice networks for point cloud processing"):
  with members:
    - "deep sets"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "octnetfusion learning depth fusion from data"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "geometric deep learning going beyond euclidean data"
    - "submanifold sparse convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "a data driven prior on facet orientation for semantic mesh labeling"):
  with members:
    - "multi view stereo with single view semantic mesh refinement"
    - "semantically informed multiview surface refinement"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "semantic 3d reconstruction with continuous regularization and ray potentials using a visibility consistency constraint"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "incremental reconstruction of urban environments by edge points delaunay triangulation"
    - "reseg a recurrent neural network based model for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "joint facade registration and segmentation for urban localization"):
  with members:
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "semantic cross view matching"
    - "learning deconvolution network for semantic segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "fully convolutional networks for semantic segmentation"
    - "tilt transform invariant low rank textures"

Connected through hyperedge HE-6 (induced by source paper "octnet learning deep 3d representations at high resolutions"):
  with members:
    - "vconv dae deep volumetric shape learning without object labels"
    - "generic 3d representation via pose estimation and matching"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"
    - "learning a predictable and generative vector representation for objects"
    - "orientation boosted voxel nets for 3d object recognition"
    - "unsupervised learning of 3d structure from images"
    - "generative and discriminative voxel modeling with convolutional neural networks"

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)