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: "radar only ego motion estimation in difficult settings via graph matching"
  Abstract: Radar detects stable, long-range objects under variable weather and lighting conditions, making it a reliable and versatile sensor well suited for ego-motion estimation. In this work, we propose a radar-only odometry pipeline that is highly robust to radar artifacts (e.g., speckle noise and false positives) and requires only one input parameter. We demonstrate its ability to adapt across diverse settings, from urban UK to off-road Iceland, achieving a scan matching accuracy of approximately 5.20 cm and 0.0929 deg when using GPS as ground truth (compared to visual odometry's 5.77 cm and 0.1032 deg). We present algorithms for keypoint extraction and data association, framing the latter as a graph matching optimization problem, and provide an in-depth system analysis.

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"
    - "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-2 (induced by source paper "look around you sequence based radar place recognition with learned rotational invariance"):
  with members:
    - "kidnapped radar topological radar localisation using rotationally invariant metric learning"
    - "under the radar learning to predict robust keypoints for odometry estimation and metric localisation in radar"
    - "making convolutional networks shift invariant again"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (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"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "driven to distraction self supervised distractor learning for robust monocular visual odometry in urban environments"
    - "the cityscapes dataset for semantic urban scene understanding"

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)