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: "driven to distraction self supervised distractor learning for robust monocular visual odometry in urban environments"
  Abstract: We present a self-supervised approach to ignoring "distractors" in camera images for the purposes of robustly estimating vehicle motion in cluttered urban environments. We leverage offline multi-session mapping approaches to automatically generate a per-pixel ephemerality mask and depth map for each input image, which we use to train a deep convolutional network. At run-time we use the predicted ephemerality and depth as an input to a monocular visual odometry (VO) pipeline, using either sparse features or dense photometric matching. Our approach yields metric-scale VO using only a single camera and can recover the correct egomotion even when 90% of the image is obscured by dynamic, independently moving objects. We evaluate our robust VO methods on more than 400km of driving from the Oxford RobotCar Dataset and demonstrate reduced odometry drift and significantly improved egomotion estimation in the presence of large moving vehicles in urban traffic.

Connected through hyperedge HE-1 (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"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "local descriptor for robust place recognition using lidar intensity"

Connected through hyperedge HE-2 (induced by source paper "mono sf multi view geometry meets single view depth for monocular scene flow estimation of dynamic traffic scenes"):
  with members:
    - "dense depth estimation of a complex dynamic scene without explicit 3d motion estimation"
    - "deep rigid instance scene flow"
    - "lightweight probabilistic deep networks"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"

Connected through hyperedge HE-3 (induced by source paper "sequential adversarial learning for self supervised deep visual odometry"):
  with members:
    - "beyond tracking selecting memory and refining poses for deep visual odometry"
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "guided feature selection for deep visual odometry"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "geometric consistency for self supervised end to end visual odometry"

Connected through hyperedge HE-4 (induced by source paper "experimental comparison of visual aided odometry methods for rail vehicles"):
  with members:
    - "appearance based landmark selection for efficient long term visual localization"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "direct sparse visual inertial odometry using dynamic marginalization"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "robust odometry using sensor consensus analysis"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"
    - "visual inertial self calibration on informative motion segments"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"

Connected through hyperedge HE-5 (induced by source paper "masking by moving learning distraction free radar odometry from pose information"):
  with members:
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "undeepvo monocular visual odometry through unsupervised deep learning"
    - "deep image homography estimation"
    - "u net convolutional networks for biomedical image segmentation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "stereo based multi motion visual odometry for mobile robots"):
  with members:
    - "motion segmentation by exploiting complementary geometric models"
    - "effective sampling fast segmentation using robust geometric model fitting"
    - "fast multi frame stereo scene flow with motion segmentation"
    - "moving object tracking employing rigid body motion on matrix lie groups"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "mot16 a benchmark for multi object tracking"
    - "direct sparse odometry"

Connected through hyperedge HE-7 (induced by source paper "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "radar only ego motion estimation in difficult settings via graph matching"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-8 (induced by source paper "every pixel counts joint learning of geometry and motion with 3d holistic understanding"):
  with members:
    - "adversarial collaboration joint unsupervised learning of depth camera motion optical flow and motion segmentation"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "lego learning edge with geometry all at once by watching videos"
    - "digging into self supervised monocular depth estimation"

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)