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 "can a robot become a movie director learning artistic principles for aerial cinematography"):
  Abstract: Aerial filming is becoming more and more popular thanks to the recent advances in drone technology. It invites many intriguing, unsolved problems at the intersection of aesthetical and scientific challenges. In this work, we propose an intelligent agent which supervises motion planning of a filming drone based on aesthetical values of video shots using deep reinforcement learning. Unlike the current state-of-the-art approaches which mostly require explicit guidance by a human expert, our drone learns how to make favorable shot type selections by experience. We propose a learning scheme which exploits aesthetical features of retrospective shots in order to extract a desirable policy for better prospective shots. We train our agent in realistic AirSim simulations using both hand-crafted and human reward functions. We deploy the same agent on a real DJI M210 drone in order to test generalization capability of our approach to real world conditions. To evaluate the success of our approach in the end, we conduct a comprehensive user study in which participants rate the shots taken using our method and write comments about them.

Includes members:
  Member "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning visuomotor policies for aerial navigation using cross modal representations")
  Member "learning agile and dynamic motor skills for legged robots":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rapidly adaptable legged robots via evolutionary meta learning")
  Member "autonomous drone cinematographer using artistic principles to create smooth safe occlusion free trajectories for aerial filming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "autonomous aerial cinematography in unstructured environments with learned artistic decision making")
  Member "optimizing for aesthetically pleasing quadrotor camera motion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "autonomous aerial cinematography in unstructured environments with learned artistic decision making")
  Member "learning robust rewards with adversarial inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust deep reinforcement learning against adversarial perturbations on observations")
  Member "airsim high fidelity visual and physical simulation for autonomous vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "control of a quadrotor with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to fly via deep model based reinforcement learning")
  Member "directing cinematographic drones":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "autonomous aerial cinematography in unstructured environments with learned artistic decision making")

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)