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: "automated cinematography with unmanned aerial vehicles"
  Abstract: The rise of Unmanned Aerial Vehicles and their increasing use in the cinema industry calls for the creation of dedicated tools. Though there is a range of techniques to automatically control drones for a variety of applications, none have considered the problem of producing cinematographic camera motion in real-time for shooting purposes. In this paper we present our approach to UAV navigation for autonomous cinematography. The contributions of this research are twofold: (i) we adapt virtual camera control techniques to UAV navigation; (ii) we introduce a drone-independent platform for high-level user interactions that integrates cinematographic knowledge. The results presented in this paper demonstrate the capacities of our tool to capture live movie scenes involving one or two moving actors.

Connected through hyperedge HE-1 (induced by source paper "cinemairsim a camera realistic robotics simulator for cinematographic purposes"):
  with members:
    - "flightgoggles photorealistic sensor simulation for perception driven robotics using photogrammetry and virtual reality"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "unrealcv connecting computer vision to unreal engine"
    - "airways optimization based planning of quadrotor trajectories according to high level user goals"
    - "generic drone control platform for autonomous capture of cinema scenes"
    - "webotstm professional mobile robot simulation"

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"
    - "can a robot become a movie director learning artistic principles for aerial cinematography"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments"):
  with members:
    - "improved generalization of heading direction estimation for aerial filming using semi supervised regression"
    - "autonomous drone cinematographer using artistic principles to create smooth safe occlusion free trajectories for aerial filming"
    - "yolov3 an incremental improvement"
    - "optimizing for aesthetically pleasing quadrotor camera motion"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "directing cinematographic drones"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"

Connected through hyperedge HE-4 (induced by source paper "can a robot become a movie director learning artistic principles for aerial cinematography"):
  with members:
    - "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments"
    - "learning agile and dynamic motor skills for legged robots"
    - "autonomous drone cinematographer using artistic principles to create smooth safe occlusion free trajectories for aerial filming"
    - "optimizing for aesthetically pleasing quadrotor camera motion"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "control of a quadrotor with reinforcement learning"
    - "directing cinematographic drones"

Connected through hyperedge HE-5 (induced by source paper "one shot imitation filming of human motion videos"):
  with members:
    - "yolov3 an incremental improvement"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "directing cinematographic drones"
    - "one shot imitation learning"
    - "temporal segment networks towards good practices for deep action recognition"
    - "towards a drone cinematographer guiding quadrotor cameras using visual composition principles"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "autonomous drone cinematographer using artistic principles to create smooth safe occlusion free trajectories for aerial filming"):
  with members:
    - "optimizing for aesthetically pleasing quadrotor camera motion"
    - "fast autonomous flight in gps denied and cluttered environments"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "directing cinematographic drones"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "airways optimization based planning of quadrotor trajectories according to high level user goals"

Connected through hyperedge HE-7 (induced by source paper "optimizing for aesthetically pleasing quadrotor camera motion"):
  with members:
    - "wyfiwyg investigating effective user support in aerial videography"
    - "airways optimization based planning of quadrotor trajectories according to high level user goals"
    - "towards a drone cinematographer guiding quadrotor cameras using visual composition principles"

Connected through hyperedge HE-8 (induced by source paper "wyfiwyg investigating effective user support in aerial videography"):
  with members:
    - "airways optimization based planning of quadrotor trajectories according to high level user goals"
    - "towards a drone cinematographer guiding quadrotor cameras using visual composition principles"

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)