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: "towards a drone cinematographer guiding quadrotor cameras using visual composition principles"
  Abstract: We present a system to capture video footage of human subjects in the real world. Our system leverages a quadrotor camera to automatically capture well-composed video of two subjects. Subjects are tracked in a large-scale outdoor environment using RTK GPS and IMU sensors. Then, given the tracked state of our subjects, our system automatically computes static shots based on well-established visual composition principles and canonical shots from cinematography literature. To transition between these static shots, we calculate feasible, safe, and visually pleasing transitions using a novel real-time trajectory planning algorithm. We evaluate the performance of our tracking system, and experimentally show that RTK GPS significantly outperforms conventional GPS in capturing a variety of canonical shots. Lastly, we demonstrate our system guiding a consumer quadrotor camera autonomously capturing footage of two subjects in a variety of use cases. This is the first end-to-end system that enables people to leverage the mobility of quadrotors, as well as the knowledge of expert filmmakers, to autonomously capture high-quality footage of people in the real world.

Connected through hyperedge HE-1 (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-2 (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-3 (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-4 (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"
    - "automated cinematography with unmanned aerial vehicles"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (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"
    - "automated cinematography with unmanned aerial vehicles"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-6 (induced by source paper "optimizing for aesthetically pleasing quadrotor camera motion"):
  with members:
    - "wyfiwyg investigating effective user support in aerial videography"
    - "automated cinematography with unmanned aerial vehicles"
    - "airways optimization based planning of quadrotor trajectories according to high level user goals"

Connected through hyperedge HE-7 (induced by source paper "wyfiwyg investigating effective user support in aerial videography"):
  with members:
    - "automated cinematography with unmanned aerial vehicles"
    - "airways optimization based planning of quadrotor trajectories according to high level user goals"

Connected through hyperedge HE-8 (induced by source paper "on realistic target coverage by autonomous drones"):
  with members:
    - "design challenges of multi uav systems in cyber physical applications a comprehensive survey and future directions"
    - "the art gallery problem is exists mathbb r complete"
    - "obstacle avoidance strategy using onboard stereo vision on a flapping wing mav"
    - "directing cinematographic drones"
    - "wigest a ubiquitous wifi based gesture recognition system"
    - "ubibreathe a ubiquitous non invasive wifi based breathing estimator"
    - "context aware computing for the internet of things a survey"

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.GR(Graphics)