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 "vision based control of a quadrotor in user proximity mediated vs end to end learning approaches"):
  Abstract: We consider the task of controlling a quadrotor to hover in front of a freely moving user, using input data from an onboard camera. On this specific task we compare two widespread learning paradigms: a mediated approach, which learns an high-level state from the input and then uses it for deriving control signals; and an end-to-end approach, which skips high-level state estimation altogether. We show that despite their fundamental difference, both approaches yield equivalent performance on this task. We finally qualitatively analyze the behavior of a quadrotor implementing such approaches.

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 "person following by autonomous robots a categorical overview":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "machine vision for improved human robot cooperation in adverse underwater conditions")
  Member "learning to navigate autonomously in outdoor environments mavnet":
    also connected through other hyperedges:
      (none)
  Member "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "aggressive perception aware navigation using deep optical flow dynamics and pixelmpc")
  Member "limits of end to end learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a deep reinforcement learning based driving policy for autonomous road vehicles")
  Member "learning to fly by crashing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "deep steering learning end to end driving model from spatial and temporal visual cues":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "energy based continuous inverse optimal control")

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)