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: "fast autonomous flight in gps denied and cluttered environments"
  Abstract: Author(s): Mohta, K.; Mulgaonkar, Y.; Watterson, M.; Liu, S.; Qu, C.; Makineni, A.; Saulnier, K.; Sun, K.; Zhu, A.; Delmerico, J.; Karydis, K.; Atanasov, N.; Loianno, G.; Scaramuzza, D.; Daniilidis, K.; Taylor, C. J.; Kumar, V.

Connected through hyperedge HE-1 (induced by source paper "reactive navigation in partially familiar planar environments using semantic perceptual feedback"):
  with members:
    - "combining optimal control and learning for visual navigation in novel environments"
    - "conceptualization of object compositions using persistent homology"
    - "admissible abstractions for near optimal task and motion planning"
    - "visual memory for robust path following"
    - "yolov3 an incremental improvement"
    - "sampling based methods for factored task and motion planning"
    - "6 dof object pose from semantic keypoints"
    - "cognitive mapping and planning for visual navigation"

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"
    - "a complete system for vision based micro aerial vehicle mapping planning and flight in cluttered environments"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-3 (induced by source paper "dfinenet ego motion estimation and depth refinement from sparse noisy depth input with rgb guidance"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "real time dense depth estimation by fusing stereo with sparse depth measurements"
    - "deep ordinal regression network for monocular depth estimation"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "sfmlearner learning monocular depth ego motion using meaningful geometric constraints"
    - "sparse and dense data with cnns depth completion and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "multi task regression based learning for autonomous unmanned aerial vehicle flight control within unstructured outdoor environments"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "veritatem dies aperit temporally consistent depth prediction enabled by a multi task geometric and semantic scene understanding approach"
    - "deep drone racing learning agile flight in dynamic environments"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "unsupervised learning of depth and ego motion from video"
    - "aggressive deep driving model predictive control with a cnn cost model"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"

Connected through hyperedge HE-5 (induced by source paper "autonomous navigation of mavs in unknown cluttered environments"):
  with members:
    - "a complete system for vision based micro aerial vehicle mapping planning and flight in cluttered environments"
    - "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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "vision based multi mav localization with anonymous relative measurements using coupled probabilistic data association filter"):
  with members:
    - "mavnet an effective semantic segmentation micro network for mav based tasks"
    - "the open vision computer an integrated sensing and compute system for mobile robots"
    - "unsupervised deep homography a fast and robust homography estimation model"

Connected through hyperedge HE-7 (induced by source paper "a complete system for vision based micro aerial vehicle mapping planning and flight in cluttered environments"):
  with members:
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "safe local exploration for replanning in cluttered unknown environments for microaerial vehicles"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "sparse 3d topological graphs for micro aerial vehicle planning"
    - "c blox a scalable and consistent tsdf based dense mapping approach"
    - "real time trajectory replanning for mavs using uniform b splines and a 3d circular buffer"
    - "nonlinear model predictive control for multi micro aerial vehicle robust collision avoidance"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"

Connected through hyperedge HE-8 (induced by source paper "autonomous drone cinematographer using artistic principles to create smooth safe occlusion free trajectories for aerial filming"):
  with members:
    - "optimizing for aesthetically pleasing quadrotor camera motion"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "directing cinematographic drones"
    - "automated cinematography with unmanned aerial vehicles"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "airways optimization based planning of quadrotor trajectories according to high level user goals"

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)