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 "danger aware weighted advantage composition of deep reinforcement learning for robot navigation"):
  Abstract: Self-navigation, referring to automatically reaching the goal while avoiding collision with obstacles, is a fundamental skill of mobile robots. Currently, Deep Reinforcement Learning (DRL) can enable the robot to navigate in a more complex environment with less computation power compared to conventional methods. However, it is time-consuming and hard to train the robot to learn goal-reaching and obstacle-avoidance skills simultaneously using DRL-based algorithms. In this paper, two Dueling Deep Q Networks (DQN) named Goal Network and Avoidance Network are used to learn the goal-reaching and obstacle-avoidance skills individually. A novel method named danger-aware advantage composition is proposed to fuse the two networks together without any redesigning and retraining. The composed Navigation Network can enable the robot to reach the goal right behind the wall and to navigate in unknown complexed environment safely and quickly.

Includes members:
  Member "multi advisor reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "danger aware adaptive composition of drl agents for self navigation")
  Member "learning with training wheels speeding up training with a simple controller for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "danger aware adaptive composition of drl agents for self navigation")
  Member "towards monocular vision based obstacle avoidance through deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "hybrid reward architecture for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "socially aware motion planning with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "social navigation with human empowerment driven reinforcement learning")
  Member "uncertainty aware reinforcement learning for collision avoidance":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "planet of the bayesians reconsidering and improving deep planning network by incorporating bayesian inference")
  Member "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "extending the openai gym for robotics a toolkit for reinforcement learning using ros and gazebo":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "danger aware adaptive composition of drl agents for self navigation")

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)