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: "aggressive quadrotor flight through narrow gaps with onboard sensing and computing"
  Abstract: In this paper, we address one of the main challenges towards autonomous quadrotor flight in complex environments, which is flight through narrow gaps. We present a method that allows a quadrotor to autonomously and safely pass through a narrow, inclined gap using only its onboard visual-inertial sensors and computer. Previous works have addressed quadrotor flight through gaps using external motion-capture systems for state estimation. Instead, we estimate the state by fusing gap detection from a single onboard camera with an IMU. Our method generates a trajectory that considers geometric, dynamic, and perception constraints: during the approach maneuver, the quadrotor always faces the gap to allow state estimation, while respecting the vehicle dynamics; during the traverse through the gap, the distance of the quadrotor to the edges of the gap is maximized. Furthermore, we replan the trajectory during its execution to cope with the varying uncertainty of the state estimate. We successfully evaluate and demonstrate the proposed approach in many real experiments, achieving a success rate of 80% and gap orientations up to 45°. To the best of our knowledge, this is the first work that addresses and successfully reports aggressive flight through narrow gaps using only onboard sensing and computing.

Connected through hyperedge HE-1 (induced by source paper "pampc perception aware model predictive control for quadrotors"):
  with members:
    - "effective target aware visual navigation for uavs"
    - "linear vs nonlinear mpc for trajectory tracking applied to rotary wing micro aerial vehicles"

Connected through hyperedge HE-2 (induced by source paper "how hard is it to cross the room training recurrent neural networks to steer a uav"):
  with members:
    - "cad2rl real single image flight without a single real image"
    - "end to end learning for self driving cars"
    - "robust monocular flight in cluttered outdoor environments"
    - "flownet learning optical flow with convolutional networks"
    - "adam a method for stochastic optimization"
    - "depth map prediction from a single image using a multi scale deep network"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

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)