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 "learning to fly via deep model based reinforcement learning"):
  Abstract: Learning to control robots without requiring models has been a long-term goal, promising diverse and novel applications. Yet, reinforcement learning has only achieved limited impact on real-time robot control due to its high demand of real-world interactions. In this work, by leveraging a learnt probabilistic model of drone dynamics, we achieve human-like quadrotor control through model-based reinforcement learning. No prior knowledge of the flight dynamics is assumed; instead, a sequential latent variable model, used generatively and as an online filter, is learnt from raw sensory input. The controller and value function are optimised entirely by propagating stochastic analytic gradients through generated latent trajectories. We show that "learning to fly" can be achieved with less than 30 minutes of experience with a single drone, and can be deployed solely using onboard computational resources and sensors, on a self-built drone.

Includes members:
  Member "deep drone racing from simulation to reality with domain randomization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep reinforcement learning with linear quadratic regulator regions")
  Member "solving rubik s cube with a robot hand":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "imagined value gradients model based policy optimization with transferable latent dynamics models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dream to control learning behaviors by latent imagination")
  Member "flying through a narrow gap using neural network an end to end planning and control approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "airsim drone racing lab")
  Member "teach repeat replan a complete and robust system for aggressive flight in complex environments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "alternating minimization based trajectory generation for quadrotor aggressive flight")
  Member "neuroflight next generation flight control firmware":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "flight controller synthesis via deep reinforcement learning")
  Member "robust and efficient quadrotor trajectory generation for fast autonomous flight":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "region of interest segmentation from lidar point cloud for multirotor aerial vehicles")
  Member "real time reinforcement learning":
    also connected through other hyperedges:
      (none)

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)