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 point clouds with self attention and gumbel subset sampling"
  Abstract: Geometric deep learning is increasingly important thanks to the popularity of 3D sensors. Inspired by the recent advances in NLP domain, the self-attention transformer is introduced to consume the point clouds. We develop Point Attention Transformers (PATs), using a parameter-efficient Group Shuffle Attention (GSA) to replace the costly Multi-Head Attention. We demonstrate its ability to process size-varying inputs, and prove its permutation equivariance. Besides, prior work uses heuristics dependence on the input data (e.g., Furthest Point Sampling) to hierarchically select subsets of input points. Thereby, we for the first time propose an end-to-end learnable and task-agnostic sampling operation, named Gumbel Subset Sampling (GSS), to select a representative subset of input points. Equipped with Gumbel-Softmax, it produces a "soft" continuous subset in training phase, and a "hard" discrete subset in test phase. By selecting representative subsets in a hierarchical fashion, the networks learn a stronger representation of the input sets with lower computation cost. Experiments on classification and segmentation benchmarks show the effectiveness and efficiency of our methods. Furthermore, we propose a novel application, to process event camera stream as point clouds, and achieve a state-of-the-art performance on DVS128 Gesture Dataset.

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 "non local part aware point cloud denoising"):
  with members:
    - "differentiable surface splatting for point based geometry processing"
    - "pointcleannet learning to denoise and remove outliers from dense point clouds"
    - "l2g auto encoder understanding point clouds by local to global reconstruction with hierarchical self attention"
    - "kpconv flexible and deformable convolution for point clouds"
    - "pu gan a point cloud upsampling adversarial network"
    - "shellnet efficient point cloud convolutional neural networks using concentric shells statistics"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "noise2void learning denoising from single noisy images"

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 "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"
    - "point voxel cnn for efficient 3d deep learning"
    - "shellnet efficient point cloud convolutional neural networks using concentric shells statistics"
    - "relation shape convolutional neural network for point cloud analysis"

Connected through hyperedge HE-5 (induced by source paper "samplenet differentiable point cloud sampling"):
  with members:
    - "adaptive hierarchical down sampling for point cloud classification"
    - "extending adversarial attacks and defenses to deep 3d point cloud classifiers"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "unsupervised detection of distinctive regions on 3d shapes"
    - "logan unpaired shape transform in latent overcomplete space"
    - "kpconv flexible and deformable convolution for point clouds"
    - "pcrnet point cloud registration network using pointnet encoding"
    - "unpaired point cloud completion on real scans using adversarial training"

Connected through hyperedge HE-6 (induced by source paper "point2spatialcapsule aggregating features and spatial relationships of local regions on point clouds using spatial aware capsules"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "parts4feature learning 3d global features from generally semantic parts in multiple views"
    - "3dviewgraph learning global features for 3d shapes from a graph of unordered views with attention"
    - "octree guided cnn with spherical kernels for 3d point clouds"
    - "associatively segmenting instances and semantics in point clouds"
    - "relation shape convolutional neural network for point cloud analysis"
    - "multi angle point cloud vae unsupervised feature learning for 3d point clouds from multiple angles by joint self reconstruction and half to half prediction"
    - "3d point capsule networks"

Connected through hyperedge HE-7 (induced by source paper "point2node correlation learning of dynamic node for point cloud feature modeling"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "point2sequence learning the shape representation of 3d point clouds with an attention based sequence to sequence network"
    - "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"
    - "tangent convolutions for dense prediction in 3d"

Connected through hyperedge HE-8 (induced by source paper "geometry sharing network for 3d point cloud classification and segmentation"):
  with members:
    - "flex convolution million scale point cloud learning beyond grid worlds"
    - "multiresolution tree networks for 3d point cloud processing"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "local spectral graph convolution for point set feature learning"
    - "deep sets"
    - "so net self organizing network for point cloud analysis"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"

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)