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: "monte carlo convolution for learning on non uniformly sampled point clouds"
  Abstract: Deep learning systems extensively use convolution operations to process input data. Though convolution is clearly defined for structured data such as 2D images or 3D volumes, this is not true for other data types such as sparse point clouds. Previous techniques have developed approximations to convolutions for restricted conditions. Unfortunately, their applicability is limited and cannot be used for general point clouds. We propose an efficient and effective method to learn convolutions for non-uniformly sampled point clouds, as they are obtained with modern acquisition techniques. Learning is enabled by four key novelties: first, representing the convolution kernel itself as a multilayer perceptron; second, phrasing convolution as a Monte Carlo integration problem, third, using this notion to combine information from multiple samplings at different levels; and fourth using Poisson disk sampling as a scalable means of hierarchical point cloud learning. The key idea across all these contributions is to guarantee adequate consideration of the underlying non-uniform sample distribution function from a Monte Carlo perspective. To make the proposed concepts applicable to real-world tasks, we furthermore propose an efficient implementation which significantly reduces the GPU memory required during the training process. By employing our method in hierarchical network architectures we can outperform most of the state-of-the-art networks on established point cloud segmentation, classification and normal estimation benchmarks. Furthermore, in contrast to most existing approaches, we also demonstrate the robustness of our method with respect to sampling variations, even when training with uniformly sampled data only. To support the direct application of these concepts, we provide a ready-to-use TensorFlow implementation of these layers at https://github.com/viscom-ulm/MCCNN.

Connected through hyperedge HE-1 (induced by source paper "pointasnl robust point clouds processing using nonlocal neural networks with adaptive sampling"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "pointcleannet learning to denoise and remove outliers from dense point clouds"
    - "kpconv flexible and deformable convolution for point clouds"
    - "hierarchical point edge interaction network for point cloud semantic segmentation"
    - "relation shape convolutional neural network for point cloud analysis"
    - "deepgcns can gcns go as deep as cnns"
    - "point convolutional neural networks by extension operators"

Connected through hyperedge HE-2 (induced by source paper "fpconv learning local flattening for point convolution"):
  with members:
    - "kpconv flexible and deformable convolution for point clouds"
    - "hierarchical point edge interaction network for point cloud semantic segmentation"
    - "deepgcns can gcns go as deep as cnns"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "a lidar point cloud generator from a virtual world to autonomous driving"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "pointconv deep convolutional networks on 3d point clouds"

Connected through hyperedge HE-3 (induced by source paper "a rotation invariant framework for deep point cloud analysis"):
  with members:
    - "deepicp an end to end deep neural network for 3d point cloud registration"
    - "total denoising unsupervised learning of 3d point cloud cleaning"
    - "deep unsupervised learning of 3d point clouds via graph topology inference and filtering"
    - "kpconv flexible and deformable convolution for point clouds"
    - "pu gan a point cloud upsampling adversarial network"
    - "rotation invariant convolutions for 3d point clouds deep learning"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "unsupervised multi task feature learning on point clouds"

Connected through hyperedge HE-4 (induced by source paper "fusion aware point convolution for online semantic 3d scene segmentation"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "active scene understanding via online semantic reconstruction"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "real time progressive 3d semantic segmentation for indoor scene"
    - "pointconv deep convolutional networks on 3d point clouds"

Connected through hyperedge HE-5 (induced by source paper "enabling viewpoint learning through dynamic label generation"):
  with members:
    - "spherical regression learning viewpoints surface normals and 3d rotations on n spheres"
    - "pose estimation for objects with rotational symmetry"
    - "cnns based viewpoint estimation for volume visualization"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "viewpoint selection for photographing architectures"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-6 (induced by source paper "a review on deep learning techniques for 3d sensed data classification"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "context prediction for unsupervised deep learning on point clouds"
    - "kpconv flexible and deformable convolution for point clouds"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "splatnet sparse lattice networks for point cloud processing"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "so net self organizing network for point cloud analysis"
    - "adversarial autoencoders for compact representations of 3d point clouds"

Connected through hyperedge HE-7 (induced by source paper "unsupervised multi task feature learning on point clouds"):
  with members:
    - "3d point capsule networks"
    - "clustering with deep learning taxonomy and new methods"
    - "multiresolution tree networks for 3d point cloud processing"
    - "deep clustering for unsupervised learning of visual features"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "splatnet sparse lattice networks for point cloud processing"
    - "local spectral graph convolution for point set feature learning"

Connected through hyperedge HE-8 (induced by source paper "human action recognition with multi laplacian graph convolutional networks"):
  with members:
    - "graph neural networks with convolutional arma filters"
    - "simplifying graph convolutional networks"
    - "graph wavelet neural network"
    - "graph convolutional neural networks via scattering"
    - "a comprehensive survey on graph neural networks"
    - "part based graph convolutional network for action recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "predict then propagate graph neural networks meet personalized pagerank"

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)