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: "efficient continuous time slam for 3d lidar based online mapping"
  Abstract: Modern 3D laser-range scanners have a high data rate, making online simultaneous localization and mapping (SLAM) computationally challenging. Recursive state estimation techniques are efficient but commit to a state estimate immediately after a new scan is made, which may lead to misalignments of measurements. We present a 3D SLAM approach that allows for refining alignments during online mapping. Our method is based on efficient local mapping and a hierarchical optimization back-end. Measurements of a 3D laser scanner are aggregated in local multiresolution maps by means of surfel-based registration. The local maps are used in a multi-level graph for allocentric mapping and localization. In order to incorporate corrections when refining the alignment, the individual 3D scans in the local map are modeled as a sub-graph and graph optimization is performed to account for drift and misalignments in the local maps. Furthermore, in each sub-graph, a continuous-time representation of the sensor trajectory allows to correct measurements between scan poses. We evaluate our approach in multiple experiments by showing qualitative results. Furthermore, we quantify the map quality by an entropy-based measure.

Connected through hyperedge HE-1 (induced by source paper "3d point cloud processing and learning for autonomous driving"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "self driving cars a survey"
    - "governing autonomous vehicles emerging responses for safety liability privacy cybersecurity and industry risks"
    - "pixor real time 3d object detection from point clouds"
    - "deep unsupervised learning of 3d point clouds via graph topology inference and filtering"
    - "3d point cloud denoising via deep neural network based local surface estimation"
    - "fvnet 3d front view proposal generation for real time object detection from point clouds"

Connected through hyperedge HE-2 (induced by source paper "da4ad end to end deep attention aware features aided visual localization for autonomous driving"):
  with members:
    - "enforcenet monocular camera localization in large scale indoor sparse lidar point cloud"
    - "sparse to dense hypercolumn matching for long term visual localization"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "vizard reliable visual localization for autonomous vehicles in urban outdoor environments"
    - "deep auxiliary learning for visual localization and odometry"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "local descriptors optimized for average precision"
    - "from coarse to fine robust hierarchical localization at large scale"

Connected through hyperedge HE-3 (induced by source paper "person in wifi fine grained person perception using wifi"):
  with members:
    - "joint activity recognition and indoor localization with wifi fingerprints"
    - "csi net unified human body characterization and action recognition"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "simple baselines for human pose estimation and tracking"
    - "efficient online multi person 2d pose tracking with recurrent spatio temporal affinity fields"
    - "characterization of a rs lidar for 3d perception"
    - "combining lidar space clustering and convolutional neural networks for pedestrian detection"
    - "towards accurate multi person pose estimation in the wild"

Connected through hyperedge HE-4 (induced by source paper "semi supervised semantic mapping through label propagation with semantic texture meshes"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "splatnet sparse lattice networks for point cloud processing"
    - "tangent convolutions for dense prediction in 3d"
    - "semantic visual localization"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "annotating object instances with a polygon rnn"

Connected through hyperedge HE-5 (induced by source paper "deepicp an end to end deep neural network for 3d point cloud registration"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "learning depth with convolutional spatial propagation network"
    - "deeptam deep tracking and mapping"
    - "deepmapping unsupervised map estimation from multiple point clouds"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "end to end learning of keypoint detector and descriptor for pose invariant 3d matching"
    - "imls slam scan to model matching based on 3d data"
    - "hierarchical discrete distribution decomposition for match density estimation"

Connected through hyperedge HE-6 (induced by source paper "detection and tracking of small objects in sparse 3d laser range data"):
  with members:
    - "fast autonomous flight in warehouses for inventory applications"
    - "team nimbro at mbzirc 2017 autonomous valve stem turning using a wrench"
    - "online visual robot tracking and identification using deep lstm networks"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "online multi target tracking using recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "in2laama inertial lidar localisation autocalibration and mapping"):
  with members:
    - "imls slam scan to model matching based on 3d data"
    - "elastic lidar fusion dense map centric continuous time slam"
    - "a simple observer for gyro and accelerometer biases in land navigation systems"
    - "an intuitive approach to inertial sensor bias estimation"

Connected through hyperedge HE-8 (induced by source paper "line based camera pose estimation in point cloud of structured environments"):
  with members:
    - "fast 3d line segment detection from unorganized point cloud"
    - "learning attraction field representation for robust line segment detection"
    - "lidar camera calibration using 3d 3d point correspondences"

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.RO(Robotics)