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: "modeling local geometric structure of 3d point clouds using geo cnn"
  Abstract: Recent advances in deep convolutional neural networks (CNNs) have motivated researchers to adapt CNNs to directly model points in 3D point clouds. Modeling local structure has been proven to be important for the success of convolutional architectures, and researchers exploited the modeling of local point sets in the feature extraction hierarchy. However, limited attention has been paid to explicitly model the geometric structure amongst points in a local region. To address this problem, we propose Geo-CNN, which applies a generic convolution-like operation dubbed as GeoConv to each point and its local neighborhood. Local geometric relationships among points are captured when extracting edge features between the center and its neighboring points. We first decompose the edge feature extraction process onto three orthogonal bases, and then aggregate the extracted features based on the angles between the edge vector and the bases. This encourages the network to preserve the geometric structure in Euclidean space throughout the feature extraction hierarchy. GeoConv is a generic and efficient operation that can be easily integrated into 3D point cloud analysis pipelines for multiple applications. We evaluate Geo-CNN on ModelNet40 and KITTI and achieve state-of-the-art performance.

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 "on isometry robustness of deep 3d point cloud models under adversarial attacks"):
  with members:
    - "adversarial attack and defense on point sets"
    - "effective rotation invariant point cnn with spherical harmonics kernels"
    - "srinet learning strictly rotation invariant representations for point cloud classification and segmentation"
    - "robustness of 3d deep learning in an adversarial setting"
    - "theoretically principled trade off between robustness and accuracy"
    - "rotation invariant convolutions for 3d point clouds deep learning"
    - "relation shape convolutional neural network for point cloud analysis"
    - "adversarial spheres"

Connected through hyperedge HE-3 (induced by source paper "randla net efficient semantic segmentation of large scale point clouds"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "pcan 3d attention map learning using contextual information for point cloud based retrieval"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "pixor real time 3d object detection from point clouds"
    - "octree guided cnn with spherical kernels for 3d point clouds"

Connected through hyperedge HE-4 (induced by source paper "point voxel cnn for efficient 3d deep learning"):
  with members:
    - "searching for mobilenetv3"
    - "voxsegnet volumetric cnns for semantic part segmentation of 3d shapes"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "pointpillars fast encoders for object detection from point clouds"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "splatnet sparse lattice networks for point cloud processing"

Connected through hyperedge HE-5 (induced by source paper "grid gcn for fast and scalable point cloud learning"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "point cloud oversegmentation with graph structured deep metric learning"
    - "densepoint learning densely contextual representation for efficient point cloud processing"
    - "hplflownet hierarchical permutohedral lattice flownet for scene flow estimation on large scale point clouds"
    - "kpconv flexible and deformable convolution for point clouds"
    - "modeling point clouds with self attention and gumbel subset sampling"
    - "point voxel cnn for efficient 3d deep learning"
    - "shellnet efficient point cloud convolutional neural networks using concentric shells statistics"

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"
    - "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"
    - "squeeze and excitation networks"

Connected through hyperedge HE-7 (induced by source paper "geometry aware generation of adversarial and cooperative point clouds"):
  with members:
    - "geonet deep geodesic networks for point cloud analysis"
    - "adversarial attack and defense on point sets"
    - "adversarial sensor attack on lidar based perception in autonomous driving"
    - "extending adversarial attacks and defenses to deep 3d point cloud classifiers"
    - "meshadv adversarial meshes for visual recognition"
    - "robustness of 3d deep learning in an adversarial setting"
    - "a skeleton bridged deep learning approach for generating meshes of complex topologies from single rgb images"
    - "adversarial point perturbations on 3d objects"

Connected through hyperedge HE-8 (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"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "dynamic graph cnn for learning on point clouds"
    - "spnet deep 3d object classification and retrieval using stereographic projection"
    - "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)