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: "spnet deep 3d object classification and retrieval using stereographic projection"
  Abstract: We propose an efficient Stereographic Projection Neural Network (SPNet) for learning representations of 3D objects. We first transform a 3D input volume into a 2D planar image using stereographic projection. We then present a shallow 2D convolutional neural network (CNN) to estimate the object category followed by view ensemble, which combines the responses from multiple views of the object to further enhance the predictions. Specifically, the proposed approach consists of four stages: (1) Stereographic projection of a 3D object, (2) view-specific feature learning, (3) view selection and (4) view ensemble. The proposed approach performs comparably to the state-of-the-art methods while having substantially lower GPU memory as well as network parameters. Despite its lightness, the experiments on 3D object classification and shape retrievals demonstrate the high performance of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "revisiting point cloud classification a new benchmark dataset and classification model on real world data"):
  with members:
    - "flex convolution million scale point cloud learning beyond grid worlds"
    - "y 2seq2seq cross modal representation learning for 3d shape and text by joint reconstruction and prediction of view and word sequences"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "local spectral graph convolution for point set feature learning"
    - "point2sequence learning the shape representation of 3d point clouds with an attention based sequence to sequence network"
    - "deep sets"
    - "learning to drive from simulation without real world labels"

Connected through hyperedge HE-2 (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"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "squeeze and excitation networks"

Connected through hyperedge HE-3 (induced by source paper "equivariant multi view networks"):
  with members:
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "a deeper look at 3d shape classifiers"
    - "3d g cnns for pulmonary nodule detection"
    - "cubenet equivariance to 3d rotation and translation"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "a general theory of equivariant cnns on homogeneous spaces"
    - "pvnet a joint convolutional network of point cloud and multi view for 3d shape recognition"
    - "polar transformer networks"

Connected through hyperedge HE-4 (induced by source paper "deeppoint3d learning discriminative local descriptors using deep metric learning on 3d point clouds"):
  with members:
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "semantic3d net a new large scale point cloud classification benchmark"
    - "drought stress classification using 3d plant models"
    - "learning compact geometric features"
    - "large scale novel object discovery in 3d"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"
    - "octnet learning deep 3d representations at high resolutions"

Connected through hyperedge HE-5 (induced by source paper "linked dynamic graph cnn learning on point cloud via linking hierarchical features"):
  with members:
    - "directional pointnet 3d environmental classification for wearable robotics"
    - "sensor fusion for predictive control of human prosthesis environment dynamics in assistive walking a survey"
    - "voxsegnet volumetric cnns for semantic part segmentation of 3d shapes"
    - "graph neural networks a review of methods and applications"
    - "modeling local geometric structure of 3d point clouds using geo cnn"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "dynamic graph cnn for learning on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

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)