human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "visual model predictive localization for computationally efficient autonomous racing of a 72 gram drone"):
  Abstract: Drone racing is becoming a popular e-sport all over the world, and beating the best human drone race pilots has quickly become a new major challenge for artificial intelligence and robotics. In this paper, we propose a strategy for autonomous drone racing which is computationally more efficient than navigation methods like visual inertial odometry and simultaneous localization and mapping. This fast light-weight vision-based navigation algorithm estimates the position of the drone by fusing race gate detections with model dynamics predictions. Theoretical analysis and simulation results show the clear advantage compared to Kalman filtering when dealing with the relatively low frequency visual updates and occasional large outliers that occur in fast drone racing. Flight tests are performed on a tiny racing quadrotor named "Trashcan", which was equipped with a Jevois smart-camera for a total of 72g. The test track consists of 3 laps around a 4-gate racing track. The gates spaced 4 meters apart and can be displaced from their supposed position. An average speed of 2m/s is achieved while the maximum speed is 2.6m/s. To the best of our knowledge, this flying platform is the smallest autonomous racing drone in the world and is 6 times lighter than the existing lightest autonomous racing drone setup (420g), while still being one of the fastest autonomous racing drones in the world.

Includes members:
  Member "deep drone racing learning agile flight in dynamic environments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to fly via deep model based reinforcement learning")
  Member "gapflyt active vision based minimalist structure less gap detection for quadrotor flight":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to seek deep reinforcement learning for phototaxis of a nano drone in an obstacle field")
  Member "autonomous drone race a computationally efficient vision based navigation and control strategy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep drone racing from simulation to reality with domain randomization")
  Member "beauty and the beast optimal methods meet learning for drone racing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep drone racing from simulation to reality with domain randomization")
  Member "aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep drone racing from simulation to reality with domain randomization")
  Member "efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hundred drones land in a minute")

Question: Please tell me which class the source paper 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)