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: "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.

Connected through hyperedge HE-1 (induced by source paper "keyfilter aware real time uav object tracking"):
  with members:
    - "learning aberrance repressed correlation filters for real time uav tracking"
    - "unsupervised deep tracking"
    - "boundary effect aware visual tracking for uav with online enhanced background learning and multi frame consensus verification"
    - "learning spatial temporal regularized correlation filters for visual tracking"
    - "kernel cross correlator"
    - "integrating boundary and center correlation filters for visual tracking with aspect ratio variation"
    - "end to end representation learning for correlation filter based tracking"
    - "learning background aware correlation filters for visual tracking"

Connected through hyperedge HE-2 (induced by source paper "autonomous aerial cinematography in unstructured environments with learned artistic decision making"):
  with members:
    - "improved generalization of heading direction estimation for aerial filming using semi supervised regression"
    - "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments"
    - "autonomous drone cinematographer using artistic principles to create smooth safe occlusion free trajectories for aerial filming"
    - "integrating kinematics and environment context into deep inverse reinforcement learning for predicting off road vehicle trajectories"
    - "optimizing for aesthetically pleasing quadrotor camera motion"
    - "good semi supervised learning that requires a bad gan"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"

Connected through hyperedge HE-3 (induced by source paper "augmented memory for correlation filters in real time uav tracking"):
  with members:
    - "learning aberrance repressed correlation filters for real time uav tracking"
    - "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments"
    - "boundary effect aware visual tracking for uav with online enhanced background learning and multi frame consensus verification"
    - "target aware deep tracking"
    - "the unmanned aerial vehicle benchmark object detection and tracking"
    - "learning spatial temporal regularized correlation filters for visual tracking"
    - "kernel cross correlator"
    - "integrating boundary and center correlation filters for visual tracking with aspect ratio variation"

Connected through hyperedge HE-4 (induced by source paper "end to end motion planning of quadrotors using deep reinforcement learning"):
  with members:
    - "cubeslam monocular 3 d object slam"
    - "accurate tracking of aggressive quadrotor trajectories using incremental nonlinear dynamic inversion and differential flatness"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "robust stereo visual inertial odometry for fast autonomous flight"
    - "adam a method for stochastic optimization"

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)