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: "autonomous drone race a computationally efficient vision based navigation and control strategy"
  Abstract: Drone racing is becoming a popular sport where human pilots have to control their drones to fly at high speed through complex environments and pass a number of gates in a pre-defined sequence. In this paper, we develop an autonomous system for drones to race fully autonomously using only onboard resources. Instead of commonly used visual navigation methods, such as simultaneous localization and mapping and visual inertial odometry, which are computationally expensive for micro aerial vehicles (MAVs), we developed the highly efficient snake gate detection algorithm for visual navigation, which can detect the gate at 20HZ on a Parrot Bebop drone. Then, with the gate detection result, we developed a robust pose estimation algorithm which has better tolerance to detection noise than a state-of-the-art perspective-n-point method. During the race, sometimes the gates are not in the drone's field of view. For this case, a state prediction-based feed-forward control strategy is developed to steer the drone to fly to the next gate. Experiments show that the drone can fly a half-circle with 1.5m radius within 2 seconds with only 30cm error at the end of the circle without any position feedback. Finally, the whole system is tested in a complex environment (a showroom in the faculty of Aerospace Engineering, TU Delft). The result shows that the drone can complete the track of 15 gates with a speed of 1.5m/s which is faster than the speeds exhibited at the 2016 and 2017 IROS autonomous drone races.

Connected through hyperedge HE-1 (induced by source paper "deep drone racing from simulation to reality with domain randomization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep drone racing learning agile flight in dynamic environments"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "beauty and the beast optimal methods meet learning for drone racing"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"
    - "teaching uavs to race end to end regression of agile controls in simulation"

Connected through hyperedge HE-2 (induced by source paper "visual model predictive localization for computationally efficient autonomous racing of a 72 gram drone"):
  with members:
    - "deep drone racing learning agile flight in dynamic environments"
    - "gapflyt active vision based minimalist structure less gap detection for quadrotor flight"
    - "beauty and the beast optimal methods meet learning for drone racing"
    - "aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision"
    - "efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone"

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)