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: "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
  Abstract: In this paper we present The Oxford Radar RobotCar Dataset, a new dataset for researching scene understanding using Millimetre-Wave FMCW scanning radar data. The target application is autonomous vehicles where this modality remains unencumbered by environmental conditions such as fog, rain, snow, or lens flare, which typically challenge other sensor modalities such as vision and LIDAR. #R##N#The data were gathered in January 2019 over thirty-two traversals of a central Oxford route spanning a total of 280 km of urban driving. It encompasses a variety of weather, traffic, and lighting conditions. This 4.7 TB dataset consists of over 240,000 scans from a Navtech CTS350-X radar and 2.4 million scans from two Velodyne HDL-32E 3D LIDARs; along with six cameras, two 2D LIDARs, and a GPS/INS receiver. In addition we release ground truth optimised radar odometry to provide an additional impetus to research in this domain. The full dataset is available for download at: this http URL

Connected through hyperedge HE-1 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation"):
  with members:
    - "badgr an autonomous self supervised learning based navigation system"
    - "kidnapped radar topological radar localisation using rotationally invariant metric learning"
    - "a hybrid compact neural architecture for visual place recognition"
    - "levelling the playing field a comprehensive comparison of visual place recognition approaches under changing conditions"
    - "beyond photometric loss for self supervised ego motion estimation"
    - "towards navigation without precise localization weakly supervised learning of goal directed navigation cost map"
    - "self supervised learning of depth and motion under photometric inconsistency"
    - "learning to follow directions in street view"

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"
    - "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"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"

Connected through hyperedge HE-3 (induced by source paper "rodnet object detection under severe conditions using vision radio cross modal supervision"):
  with members:
    - "video instance segmentation"
    - "centernet keypoint triplets for object detection"
    - "tracking without bells and whistles"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "moana an online learned adaptive appearance model for robust multiple object tracking in 3d"
    - "yolov3 an incremental improvement"
    - "exploit the connectivity multi object tracking with trackletnet"

Connected through hyperedge HE-4 (induced by source paper "rsl net localising in satellite images from a radar on the ground"):
  with members:
    - "masking by moving learning distraction free radar odometry from pose information"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "conditional image to image translation"
    - "adversarial training for adverse conditions robust metric localisation using appearance transfer"
    - "learning steerable filters for rotation equivariant cnns"
    - "polar transformer networks"
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning less is more 6d camera localization via 3d surface regression"

Connected through hyperedge HE-5 (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"
    - "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"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "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-6 (induced by source paper "under the radar learning to predict robust keypoints for odometry estimation and metric localisation in radar"):
  with members:
    - "masking by moving learning distraction free radar odometry from pose information"
    - "key net keypoint detection by handcrafted and learned cnn filters"
    - "deep closest point learning representations for point cloud registration"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "superpoint self supervised interest point detection and description"
    - "toward geometric deep slam"
    - "undeepvo monocular visual odometry through unsupervised deep learning"
    - "lift learned invariant feature transform"

Connected through hyperedge HE-7 (induced by source paper "real time kinematic ground truth for the oxford robotcar dataset"):
  with members:
    - "eu long term dataset with multiple sensors for autonomous driving"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-8 (induced by source paper "lidar lateral localisation despite challenging occlusion from traffic"):
  with members:
    - "kidnapped radar topological radar localisation using rotationally invariant metric learning"
    - "online inference and detection of curbs in partially occluded scenes with sparse lidar"

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)