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: "point voxel cnn for efficient 3d deep learning"
  Abstract: We present Point-Voxel CNN (PVCNN) for efficient, fast 3D deep learning. Previous work processes 3D data using either voxel-based or point-based NN models. However, both approaches are computationally inefficient. The computation cost and memory footprints of the voxel-based models grow cubically with the input resolution, making it memory-prohibitive to scale up the resolution. As for point-based networks, up to 80% of the time is wasted on structuring the irregular data which have rather poor memory locality, not on the actual feature extraction. In this paper, we propose PVCNN that represents the 3D input data in points to reduce the memory consumption, while performing the convolutions in voxels to largely reduce the irregular data access and improve the locality. Our PVCNN model is both memory and computation efficient. Evaluated on semantic and part segmentation datasets, it achieves much higher accuracy than the voxel-based baseline with 10x GPU memory reduction; it also outperforms the state-of-the-art point-based models with 7x measured speedup on average. Remarkably, narrower version of PVCNN achieves 2x speedup over PointNet (an extremely efficient model) on part and scene segmentation benchmarks with much higher accuracy. We validate the general effectiveness of our PVCNN on 3D object detection: by replacing the primitives in Frustrum PointNet with PVConv, it outperforms Frustrum PointNet++ by 2.4% mAP on average with 1.5x measured speedup and GPU memory reduction.

Connected through hyperedge HE-1 (induced by source paper "advectivenet an eulerian lagrangian fluidic reservoir for point cloud processing"):
  with members:
    - "recent advances in physical reservoir computing a review"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "3d point capsule networks"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "multi resolution 3d convolutional neural networks for object recognition"
    - "splatnet sparse lattice networks for point cloud processing"
    - "adaptive o cnn a patch based deep representation of 3d shapes"

Connected through hyperedge HE-2 (induced by source paper "skd unsupervised keypoint detection for point clouds using saliency estimation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "pcan 3d attention map learning using contextual information for point cloud based retrieval"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "elf embedded localisation of features in pre trained cnn"
    - "deep closest point learning representations for point cloud registration"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"

Connected through hyperedge HE-3 (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"
    - "shellnet efficient point cloud convolutional neural networks using concentric shells statistics"
    - "relation shape convolutional neural network for point cloud analysis"

Connected through hyperedge HE-4 (induced by source paper "pv rcnn point voxel feature set abstraction for 3d object detection"):
  with members:
    - "multi view pointnet for 3d scene understanding"
    - "std sparse to dense 3d object detector for point cloud"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "starnet targeted computation for object detection in point clouds"
    - "pixor real time 3d object detection from point clouds"
    - "patch refinement localized 3d object detection"
    - "kpconv flexible and deformable convolution for point clouds"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"

Connected through hyperedge HE-5 (induced by source paper "object as hotspots an anchor free 3d object detection approach via firing of hotspots"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "std sparse to dense 3d object detector for point cloud"
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "pixor real time 3d object detection from point clouds"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "iou loss for 2d 3d object detection"
    - "voxel fpn multi scale voxel feature aggregation in 3d object detection from point clouds"
    - "fast point r cnn"

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)