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: "visual inertial teach and repeat for aerial inspection"
  Abstract: Industrial facilities often require periodic visual inspections of key installations. Examining these points of interest is time consuming, potentially hazardous or require special equipment to reach. MAVs are ideal platforms to automate this expensive and tedious task. In this work we present a novel system that enables a human operator to teach a visual inspection task to an autonomous aerial vehicle by simply demonstrating the task using a handheld device. To enable robust operation in confined, GPS-denied environments, the system employs the Google Tango visual-inertial mapping framework as the only source of pose estimates. In a first step the operator records the desired inspection path and defines the inspection points. The mapping framework then computes a feature-based localization map, which is shared with the robot. After take-off, the robot estimates its pose based on this map and plans a smooth trajectory through the way points defined by the operator. Furthermore, the system is able to track the poses of other robots or the operator, localized in the same map, and follow them in real-time while keeping a safe distance.

Connected through hyperedge HE-1 (induced by source paper "a mobile manipulation system for one shot teaching of complex tasks in homes"):
  with members:
    - "quasi direct drive for low cost compliant robotic manipulation"
    - "the double sphere camera model"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "the devil is in the tails fine grained classification in the wild"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-2 (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"
    - "a complete system for vision based micro aerial vehicle mapping planning and flight in cluttered environments"
    - "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-3 (induced by source paper "beauty and the beast optimal methods meet learning for drone racing"):
  with members:
    - "deep drone racing learning agile flight in dynamic environments"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "teaching uavs to race end to end regression of agile controls in simulation"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "deep neural network for real time autonomous indoor navigation"

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)