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: "airways optimization based planning of quadrotor trajectories according to high level user goals"
  Abstract: In this paper we propose a computational design tool that allows end-users to create advanced quadrotor trajectories with a variety of application scenarios in mind. Our algorithm allows novice users to create quadrotor based use-cases without requiring deep knowledge in either quadrotor control or the underlying constraints of the target domain. To achieve this goal we propose an optimization-based method that generates feasible trajectories which can be flown in the real world. Furthermore, the method incorporates high-level human objectives into the planning of flight trajectories. An easy to use 3D design tool allows for quick specification and editing of trajectories as well as for intuitive exploration of the resulting solution space. We demonstrate the utility of our approach in several real-world application scenarios, including aerial-videography, robotic light-painting and drone racing.

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"
    - "automated cinematography with unmanned aerial vehicles"
    - "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 "lerop a learning based modular robot photography framework"):
  with members:
    - "the adobeindoornav dataset towards deep reinforcement learning based real world indoor robot visual navigation"
    - "in defense of the triplet loss for person re identification"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "control of a quadrotor with reinforcement learning"
    - "convolutional pose machines"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "yolo9000 better faster stronger"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"

Connected through hyperedge HE-4 (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-5 (induced by source paper "selfie drone stick a natural interface for quadcopter photography"):
  with members:
    - "customizing object detectors for indoor robots"
    - "intelligently assisting human guided quadcopter photography"
    - "autonomous quadrotor landing using deep reinforcement learning"
    - "learning to fly by crashing"
    - "prioritized experience replay"
    - "dueling network architectures for deep reinforcement learning"
    - "wider face a face detection benchmark"

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"
    - "automated cinematography with unmanned aerial vehicles"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

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"
    - "automated cinematography with unmanned aerial vehicles"
    - "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:
    - "automated cinematography with unmanned aerial vehicles"
    - "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.HC(Human-Computer Interaction)