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: "pointnetvlad deep point cloud based retrieval for large scale place recognition"
  Abstract: Unlike its image based counterpart, point cloud based retrieval for place recognition has remained as an unexplored and unsolved problem. This is largely due to the difficulty in extracting local feature descriptors from a point cloud that can subsequently be encoded into a global descriptor for the retrieval task. In this paper, we propose the PointNetVLAD where we leverage on the recent success of deep networks to solve point cloud based retrieval for place recognition. Specifically, our PointNetVLAD is a combination/modification of the existing PointNet and NetVLAD, which allows end-to-end training and inference to extract the global descriptor from a given 3D point cloud. Furthermore, we propose the "lazy triplet and quadruplet" loss functions that can achieve more discriminative and generalizable global descriptors to tackle the retrieval task. We create benchmark datasets for point cloud based retrieval for place recognition, and the experimental results on these datasets show the feasibility of our PointNetVLAD. Our code and the link for the benchmark dataset downloads are available in our project website. this http URL

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 "pointloc deep pose regressor for lidar point cloud localization"):
  with members:
    - "robust attentional aggregation of deep feature sets for multi view 3d reconstruction"
    - "local supports global deep camera relocalization with sequence enhancement"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "atloc attention guided camera localization"
    - "deep hough voting for 3d object detection in point clouds"
    - "deeppco end to end point cloud odometry through deep parallel neural network"
    - "deep auxiliary learning for visual localization and odometry"
    - "flownet3d learning scene flow in 3d point clouds"

Connected through hyperedge HE-3 (induced by source paper "kidnapped radar topological radar localisation using rotationally invariant metric learning"):
  with members:
    - "masking by moving learning distraction free radar odometry from pose information"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "making convolutional networks shift invariant again"
    - "radar only ego motion estimation in difficult settings via graph matching"
    - "a holistic visual place recognition approach using lightweight cnns for severe viewpoint and appearance changes"
    - "omnidirectional cnn for visual place recognition and navigation"
    - "local descriptor for robust place recognition using lidar intensity"
    - "driven to distraction self supervised distractor learning for robust monocular visual odometry in urban environments"

Connected through hyperedge HE-4 (induced by source paper "augmenting visual place recognition with structural cues"):
  with members:
    - "leveraging deep visual descriptors for hierarchical efficient localization"
    - "learning 3d segment descriptors for place recognition"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "pointfusion deep sensor fusion for 3d bounding box estimation"
    - "fine tuning cnn image retrieval with no human annotation"
    - "segmatch segment based place recognition in 3d point clouds"
    - "deep image retrieval learning global representations for image search"
    - "direct sparse odometry"

Connected through hyperedge HE-5 (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"
    - "modeling point clouds with self attention and gumbel subset sampling"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "look no deeper recognizing places from opposing viewpoints under varying scene appearance using single view depth estimation"):
  with members:
    - "don t look back robustifying place categorization for viewpoint and condition invariant place recognition"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "a deeper insight into the undemon unsupervised deep network for depth and ego motion estimation"
    - "eng end to end neural geometry for robust depth and pose estimation using cnns"
    - "x view graph based semantic multi view localization"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "improving condition and environment invariant place recognition with semantic place categorization"

Connected through hyperedge HE-7 (induced by source paper "lcd learned cross domain descriptors for 2d 3d matching"):
  with members:
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "2d3d matchnet learning to match keypoints across 2d image and 3d point cloud"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "learning a local feature descriptor for 3d lidar scans"

Connected through hyperedge HE-8 (induced by source paper "pointhop an explainable machine learning method for point cloud classification"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "ensembles of feedforward designed convolutional neural networks"
    - "interpretable convolutional neural networks via feedforward design"
    - "pointpillars fast encoders for object detection from point clouds"
    - "meshnet mesh neural network for 3d shape representation"
    - "dynamic graph cnn for learning on point clouds"
    - "pvnet a joint convolutional network of point cloud and multi view for 3d shape recognition"
    - "on data driven saak transform"

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)