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: "augmented reality based visualization of navigation data of mobile robots on the microsoft hololens possibilities and limitations"
  Abstract: The demand for mobile robots has rapidly increased in recent years due to the flexibility and high variety of application fields comparing to static robots. To deal with complex tasks such as navigation, they work with high amounts of different sensor data making it difficult to operate with for non-experts. To enhance user understanding and human robot interaction, we propose an approach to visualize the navigation stack within a cutting edge 3D Augmented Reality device -- the Microsoft Hololens. Therefore, relevant navigation stack data including laser scan, environment map and path planing data are visualized in 3D within the head mounted device. Based on that prototype, we evaluate the Hololens in terms of computational capabilities and limitations for dealing with huge amount of real-time data. Results show that the Hololens is capable of a proper visualization of huge amounts of sensor data. We demonstrate a proper visualization of navigation stack data in 3D within the Hololens. However, there are limitations when transferring and displaying different kinds of data simultaneously.

Connected through hyperedge HE-1 (induced by source paper "a markerless deep learning based 6 degrees of freedom poseestimation for with mobile robots using rgb data"):
  with members:
    - "a 3d deep learning based augmented reality calibration method for robotic environments using depth sensor data"
    - "deep hough voting for 3d object detection in point clouds"
    - "estimating 6d pose from localizing designated surface keypoints"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "deep 6 dof tracking"
    - "real time seamless single shot 6d object pose prediction"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-2 (induced by source paper "a 3d deep learning based augmented reality calibration method for robotic environments using depth sensor data"):
  with members:
    - "deep hough voting for 3d object detection in point clouds"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "deep 6 dof tracking"

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)