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: "gift a real time and scalable 3d shape search engine"
  Abstract: Projective analysis is an important solution for 3D shape retrieval, since human visual perceptions of 3D shapes rely on various 2D observations from different view points. Although multiple informative and discriminative views are utilized, most projection-based retrieval systems suffer from heavy computational cost, thus cannot satisfy the basic requirement of scalability for search engines. In this paper, we present a real-time 3D shape search engine based on the projective images of 3D shapes. The real-time property of our search engine results from the following aspects: (1) efficient projection and view feature extraction using GPU acceleration; (2) the first inverted file, referred as F-IF, is utilized to speed up the procedure of multi-view matching; (3) the second inverted file (S-IF), which captures a local distribution of 3D shapes in the feature manifold, is adopted for efficient context-based reranking. As a result, for each query the retrieval task can be finished within one second despite the necessary cost of IO overhead. We name the proposed 3D shape search engine, which combines GPU acceleration and Inverted File (Twice), as GIFT. Besides its high efficiency, GIFT also outperforms the state-of-the-art methods significantly in retrieval accuracy on various shape benchmarks and competitions.

Connected through hyperedge HE-1 (induced by source paper "review deep learning on 3d point clouds"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "shape2motion joint analysis of motion parts and attributes from 3d shapes"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "blvd building a large scale 5d semantics benchmark for autonomous driving"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "pixor real time 3d object detection from point clouds"
    - "densepoint learning densely contextual representation for efficient point cloud processing"

Connected through hyperedge HE-2 (induced by source paper "multiple discrimination and pairwise cnn for view based 3d object retrieval"):
  with members:
    - "hypergraph neural networks"
    - "contrastive center loss for deep neural networks"
    - "sketch based 3d shape retrieval using convolutional neural networks"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "representation learning on unit ball with 3d roto translational equivariance"):
  with members:
    - "blended convolution and synthesis for efficient discrimination of 3d shapes"
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "adversarial training of variational auto encoders for high fidelity image generation"
    - "attention based deep multiple instance learning"
    - "splatnet sparse lattice networks for point cloud processing"
    - "so net self organizing network for point cloud analysis"
    - "cubenet equivariance to 3d rotation and translation"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-4 (induced by source paper "location field descriptors single image 3d model retrieval in the wild"):
  with members:
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "triplet center loss for multi view 3d object retrieval"
    - "3d pose estimation for fine grained object categories"
    - "learning shape priors for single view 3d completion and reconstruction"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

Connected through hyperedge HE-5 (induced by source paper "coherent point drift networks unsupervised learning of non rigid point set registration"):
  with members:
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "so net self organizing network for point cloud analysis"
    - "an unsupervised learning model for deformable medical image registration"
    - "began boundary equilibrium generative adversarial networks"
    - "feastnet feature steered graph convolutions for 3d shape analysis"
    - "convolutional neural network architecture for geometric matching"
    - "deepsketch2face a deep learning based sketching system for 3d face and caricature modeling"
    - "foldingnet interpretable unsupervised learning on 3d point clouds"

Connected through hyperedge HE-6 (induced by source paper "mv c3d a spatial correlated multi view 3d convolutional neural networks"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "modeling local geometric structure of 3d point clouds using geo cnn"
    - "pvrnet point view relation neural network for 3d shape recognition"
    - "so net self organizing network for point cloud analysis"
    - "angular triplet center loss for multi view 3d shape retrieval"
    - "spnet deep 3d object classification and retrieval using stereographic projection"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-7 (induced by source paper "hplflownet hierarchical permutohedral lattice flownet for scene flow estimation on large scale point clouds"):
  with members:
    - "flownet3d learning scene flow in 3d point clouds"
    - "splatnet sparse lattice networks for point cloud processing"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "3d semantic segmentation with submanifold sparse convolutional networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-8 (induced by source paper "blended convolution and synthesis for efficient discrimination of 3d shapes"):
  with members:
    - "group normalization"
    - "adversarial training of variational auto encoders for high fidelity image generation"
    - "point convolutional neural networks by extension operators"
    - "local spectral graph convolution for point set feature learning"
    - "so net self organizing network for point cloud analysis"
    - "dynamic graph cnn for learning on point clouds"
    - "3d steerable cnns learning rotationally equivariant features in volumetric data"
    - "volumetric convolution automatic representation learning in unit ball"

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)