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: "a complete system for vision based micro aerial vehicle mapping planning and flight in cluttered environments"
  Abstract: We present an open-source system for Micro-Aerial Vehicle autonomous navigation from vision-based sensing. Our system focuses on dense mapping, safe local planning, and global trajectory generation, especially when using narrow field of view sensors in very cluttered environments. In addition, details about other necessary parts of the system and special considerations for applications in real-world scenarios are presented. We focus our experiments on evaluating global planning, path smoothing, and local planning methods on real maps made on MAVs in realistic search and rescue and industrial inspection scenarios. We also perform thousands of simulations in cluttered synthetic environments, and finally validate the complete system in real-world experiments.

Connected through hyperedge HE-1 (induced by source paper "closed loop benchmarking of stereo visual inertial slam systems understanding the impact of drift and latency on tracking accuracy"):
  with members:
    - "good feature matching towards accurate robust vo vslam with low latency"
    - "a general optimization based framework for local odometry estimation with multiple sensors"
    - "kimera an open source library for real time metric semantic localization and mapping"
    - "characterizing slam benchmarks and methods for the robust perception age"
    - "navigating the landscape for real time localization and mapping for robotics and virtual and augmented reality"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "the blackbird dataset a large scale dataset for uav perception in aggressive flight"
    - "robust stereo visual inertial odometry for fast autonomous flight"

Connected through hyperedge HE-2 (induced by source paper "a 3d reactive navigation algorithm for mobile robots by using tentacle based sampling"):
  with members:
    - "autonomous navigation of mavs in unknown cluttered environments"
    - "fast autonomous flight in warehouses for inventory applications"
    - "fast autonomous flight in gps denied and cluttered environments"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-3 (induced by source paper "teach repeat replan a complete and robust system for aggressive flight in complex environments"):
  with members:
    - "robust and efficient quadrotor trajectory generation for fast autonomous flight"
    - "fiesta fast incremental euclidean distance fields for online motion planning of aerial robots"
    - "real time scalable dense surfel mapping"
    - "visual inertial teach and repeat for aerial inspection"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "a new approach to time optimal path parameterization based on reachability analysis"
    - "topomap topological mapping and navigation based on visual slam maps"
    - "a convex optimization approach to smooth trajectories for motion planning with car like robots"

Connected through hyperedge HE-4 (induced by source paper "autonomous navigation of mavs in unknown cluttered environments"):
  with members:
    - "nanomap fast uncertainty aware proximity queries with lazy search over local 3d data"
    - "safe local exploration for replanning in cluttered unknown environments for microaerial vehicles"
    - "fast autonomous flight in gps denied and cluttered environments"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "real time trajectory replanning for mavs using uniform b splines and a 3d circular buffer"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "high speed flight in an ergodic forest"

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)