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: "voxsegnet volumetric cnns for semantic part segmentation of 3d shapes"
  Abstract: Voxel is an important format to represent geometric data, which has been widely used for 3D deep learning in shape analysis due to its generalization ability and regular data format. However, fine-grained tasks like part segmentation require detailed structural information, which increases voxel resolution and thus causes other issues such as the exhaustion of computational resources. In this paper, we propose a novel volumetric convolutional neural network, which could extract discriminative features encoding detailed information from voxelized 3D data under a limited resolution. To this purpose, a spatial dense extraction (SDE) module is designed to preserve the spatial resolution during the feature extraction procedure, alleviating the loss of detail caused by sub-sampling operations such as max-pooling. An attention feature aggregation (AFA) module is also introduced to adaptively select informative features from different abstraction scales, leading to segmentation with both semantic consistency and high accuracy of details. Experiment results on the large-scale dataset demonstrate the effectiveness of our method in 3D shape part segmentation.

Connected through hyperedge HE-1 (induced by source paper "point voxel cnn for efficient 3d deep learning"):
  with members:
    - "searching for mobilenetv3"
    - "modeling local geometric structure of 3d point clouds using geo cnn"
    - "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-2 (induced by source paper "point attention network for semantic segmentation of 3d point clouds"):
  with members:
    - "octree guided cnn with spherical kernels for 3d point clouds"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "sbnet sparse blocks network for fast inference"
    - "splatnet sparse lattice networks for point cloud processing"
    - "local spectral graph convolution for point set feature learning"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "so net self organizing network for point cloud analysis"

Connected through hyperedge HE-3 (induced by source paper "semantickitti a dataset for semantic scene understanding of lidar sequences"):
  with members:
    - "efficient semantic scene completion network with spatial group convolution"
    - "flex convolution million scale point cloud learning beyond grid worlds"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "yolov3 an incremental improvement"
    - "splatnet sparse lattice networks for point cloud processing"
    - "fully convolutional point networks for large scale point clouds"
    - "know what your neighbors do 3d semantic segmentation of point clouds"

Connected through hyperedge HE-4 (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"
    - "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"
    - "spnet deep 3d object classification and retrieval using stereographic projection"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

Connected through hyperedge HE-5 (induced by source paper "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"):
  with members:
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "virtualhome simulating household activities via programs"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "chalet cornell house agent learning environment"
    - "splatnet sparse lattice networks for point cloud processing"
    - "dynamic graph cnn for learning on point clouds"
    - "structure aware generative network for 3d shape modeling"

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)