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: "ground edge based lidar localization without a reflectivity calibration for autonomous driving"
  Abstract: In this work, we propose an alternative formulation to the problem of ground reflectivity grid-based localization involving laser-scanned data from multiple LIDARs mounted on autonomous vehicles. The driving idea of our localization formulation is an alternative edge reflectivity grid representation, which is invariant to laser source, angle of incidence, range, and robot surveying motion. Such a property eliminates the need of the postfactory reflectivity calibration whose time requirements are infeasible in mass-produced robots/vehicles. Our experiments demonstrate that we can achieve better performance than state of the art on ground reflectivity inference-map-based localization at no additional computational burden.

Connected through hyperedge HE-1 (induced by source paper "ford multi av seasonal dataset"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "localization requirements for autonomous vehicles"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-2 (induced by source paper "towards navigation without precise localization weakly supervised learning of goal directed navigation cost map"):
  with members:
    - "yolov3 an incremental improvement"
    - "variational end to end navigation and localization"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "laser map aided visual inertial localization in changing environment"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"
    - "3d ssd learning hierarchical features from rgb d images for amodal 3d object detection"
    - "robust and precise vehicle localization based on multi sensor fusion in diverse city scenes"

Connected through hyperedge HE-3 (induced by source paper "local descriptor for robust place recognition using lidar intensity"):
  with members:
    - "robust photogeometric localization over time for map centric loop closure"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "visual place recognition with probabilistic voting"
    - "x view graph based semantic multi view localization"
    - "segmatch segment based place recognition in 3d point clouds"

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)