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: "build your own visual inertial drone a cost effective and open source autonomous drone"
  Abstract: This article describes an approach to building a cost-effective and research-grade visual-inertial (VI) odometry-aided vertical takeoff and landing (VTOL) platform. We utilize an off-the-shelf VI sensor, an onboard computer, and a quadrotor platform, all of which are factory calibrated and mass produced, thereby sharing similar hardware and sensor specifications [e.g., mass, dimensions, intrinsic and extrinsic of camera-inertial measurement unit (IMU) systems, and signal-to-noise ratio]. We then perform system calibration and identification, enabling the use of our VI odometry, multisensor fusion (MSF), and model predictive control (MPC) frameworks with off-the-shelf products. This approach partially circumvents the tedious parameter-tuning procedures required to build a full system. The complete system is extensively evaluated both indoors using a motioncapture system and outdoors using a laser tracker while performing hover and step responses and trajectory-following tasks in the presence of external wind disturbances. We achieve root-mean-square (rms) pose errors of 0.036 m with respect to reference hover trajectories. We also conduct relatively long distance (.180 m) experiments on a farm site, demonstrating a 0.82% drift error of the total flight distance. This article conveys the insights we acquired about the platform and sensor module and offers open-source code with tutorial documentation to the community.

Connected through hyperedge HE-1 (induced by source paper "building an aerial ground robotics system for precision farming"):
  with members:
    - "semantic mapping for orchard environments by merging two sides reconstructions of tree rows"
    - "agricolmap aerial ground collaborative 3d mapping for precision farming"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "fully convolutional networks with sequential information for robust crop and weed detection in precision farming"
    - "an informative path planning framework for uav based terrain monitoring"
    - "an effective multi cue positioning system for agricultural robotics"
    - "weedmap a large scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming"
    - "multiresolution mapping and informative path planning for uav based terrain monitoring"

Connected through hyperedge HE-2 (induced by source paper "duckiefloat a collision tolerant resource constrained blimp for long term autonomy in subterranean environments"):
  with members:
    - "lookup vision only real time precise underground localisation for autonomous mining vehicles"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-3 (induced by source paper "ultra low power deep learning powered autonomous nano drones"):
  with members:
    - "nanomap fast uncertainty aware proximity queries with lazy search over local 3d data"
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "an iot endpoint system on chip for secure and energy efficient near sensor analytics"
    - "aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "plato policy learning using adaptive trajectory optimization"
    - "deep residual learning for image recognition"

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)