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 "selfie drone stick a natural interface for quadcopter photography"):
  Abstract: A physical selfie stick extends the user's reach, enabling the creation of personal photos that include more of the background scene. Conversely a quadcopter can capture photos at distances unattainable for the human, but teloperating a quadcopter to a good viewpoint is a non-trivial task. This paper presents a natural interface for quadcopter photography, the Selfie Drone Stick that allows the user to guide the quadcopter to the optimal vantage point based on the phone's sensors. The user points the phone once, and the quadcopter autonomously flies to the target viewpoint based on the phone camera and IMU sensor data. Visual servoing is achieved through the combination of a dense neural network object detector that matches the image captured from the phone camera to a bounding box in the scene and a Deep Q-Network controller that flies to the desired vantage point. Our deep learning architecture is trained with a combination of real-world images and simulated flight data. Integrating the deep RL controller with an intuitive interface provides a more positive user experience than a standard teleoperation paradigm.

Includes members:
  Member "customizing object detectors for indoor robots":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "incremental few shot object detection")
  Member "intelligently assisting human guided quadcopter photography":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "customizing object detectors for indoor robots")
  Member "autonomous quadrotor landing using deep reinforcement learning":
    also connected through other hyperedges:
      (none)
  Member "learning to fly by crashing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "airways optimization based planning of quadrotor trajectories according to high level user goals":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cinemairsim a camera realistic robotics simulator for cinematographic purposes")
  Member "prioritized experience replay":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "dueling network architectures for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "wider face a face detection benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generalizing face representation with unlabeled data")

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)