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: "project autovision localization and 3d scene perception for an autonomous vehicle with a multi camera system"
  Abstract: Project AutoVision aims to develop localization and 3D scene perception capabilities for a self-driving vehicle. Such capabilities will enable autonomous navigation in urban and rural environments, in day and night, and with cameras as the only exteroceptive sensors. The sensor suite employs many cameras for both 360-degree coverage and accurate multi-view stereo; the use of low-cost cameras keeps the cost of this sensor suite to a minimum. In addition, the project seeks to extend the operating envelope to include GNSS-less conditions which are typical for environments with tall buildings, foliage, and tunnels. Emphasis is placed on leveraging multi-view geometry and deep learning to enable the vehicle to localize and perceive in 3D space. This paper presents an overview of the project, and describes the sensor suite and current progress in the areas of calibration, localization, and perception.

Connected through hyperedge HE-1 (induced by source paper "evaluation of cross view matching to improve ground vehicle localization with aerial perception"):
  with members:
    - "leveraging photogrammetric mesh models for aerial ground feature point matching toward integrated 3d reconstruction"
    - "bridging the domain gap for ground to aerial image matching"
    - "lending orientation to neural networks for cross view geo localization"
    - "cross view image matching for geo localization in urban environments"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "semantic cross view matching"
    - "wide area image geolocalization with aerial reference imagery"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "redesigning slam for arbitrary multi camera systems"):
  with members:
    - "voxel map for visual slam"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "direct sparse odometry"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-3 (induced by source paper "efficient 2d 3d matching for multi camera visual localization"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "geometric loss functions for camera pose regression with deep learning"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "3d visual perception for self driving cars using a multi camera system calibration mapping localization and obstacle detection"
    - "on the fly adaptation of regression forests for online camera relocalisation"
    - "billion scale similarity search with gpus"
    - "semantic visual localization"

Connected through hyperedge HE-4 (induced by source paper "real time dense mapping for self driving vehicles using fisheye cameras"):
  with members:
    - "efficient 2d 3d matching for multi camera visual localization"
    - "yolov3 an incremental improvement"
    - "robust dense mapping for large scale dynamic environments"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "slanted stixels representing san francisco s steepest streets"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "instance aware semantic segmentation via multi task network cascades"
    - "high performance and tunable stereo reconstruction"

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)