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: "pcan 3d attention map learning using contextual information for point cloud based retrieval"
  Abstract: Point cloud based retrieval for place recognition is an emerging problem in vision field. The main challenge is how to find an efficient way to encode the local features into a discriminative global descriptor. In this paper, we propose a Point Contextual Attention Network (PCAN), which can predict the significance of each local point feature based on point context. Our network makes it possible to pay more attention to the task-relevent features when aggregating local features. Experiments on various benchmark datasets show that the proposed network can provide outperformance than current state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "geometric back projection network for point cloud classification"):
  with members:
    - "hierarchical back projection network for image super resolution"
    - "pyramnet point cloud pyramid attention network and graph embedding module for classification and segmentation"
    - "point attention network for semantic segmentation of 3d point clouds"
    - "densepoint learning densely contextual representation for efficient point cloud processing"
    - "l2g auto encoder understanding point clouds by local to global reconstruction with hierarchical self attention"
    - "kpconv flexible and deformable convolution for point clouds"
    - "real image denoising with feature attention"
    - "pu gan a point cloud upsampling adversarial network"

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"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "point voxel cnn for efficient 3d deep learning"
    - "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 "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"
    - "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"
    - "concrete autoencoders for differentiable feature selection and reconstruction"

Connected through hyperedge HE-4 (induced by source paper "skd unsupervised keypoint detecting for point clouds using embedded 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"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "elf embedded localisation of features in pre trained cnn"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "end to end learning of keypoint detector and descriptor for pose invariant 3d matching"
    - "generating 3d adversarial point clouds"

Connected through hyperedge HE-5 (induced by source paper "geometric feedback network for point cloud classification"):
  with members:
    - "pyramnet point cloud pyramid attention network and graph embedding module for classification and segmentation"
    - "kpconv flexible and deformable convolution for point clouds"
    - "real image denoising with feature attention"
    - "gapnet graph attention based point neural network for exploiting local feature of point cloud"
    - "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"
    - "point2sequence learning the shape representation of 3d point clouds with an attention based sequence to sequence network"

Connected through hyperedge HE-6 (induced by source paper "global visual localization in lidar maps through shared 2d 3d embedding space"):
  with members:
    - "2d3d matchnet learning to match keypoints across 2d image and 3d point cloud"
    - "cmrnet camera to lidar map registration"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "dynamic graph cnn for learning on point clouds"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "a survey of structure from motion"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "netvlad cnn architecture for weakly supervised place recognition"

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)