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 "map less navigation a single drl based controller for robots with varied dimensions"):
  Abstract: Deep reinforcement learning (DRL) has shown great potential in training control agents for map-less robot navigation. However, the trained agents are generally dependent on the employed robot in training or dimension-specific, which cannot be directly reused by robots with different dimensional configurations. To address this issue, a novel DRL-based robot navigation method is proposed in this paper. The proposed approach trains a meta-robot with DRL and then transfers the meta-skill to a robot with a different dimensional configuration (named dimension-scaled robot) using a method named dimension-variable skill transfer (DVST), referred to as DRL-DVST. During the training phase, the meta-agent learns to perform self-navigation with the meta-robot in a simulation environment. In the skill-transfer phase, the observations of the dimension-scaled robot are transferred to the meta-agent in a scaled manner, and the control policy generated by the meta-agent is scaled back to the dimension-scaled robot. Simulation and real-world experimental results indicate that robots with different sizes and angular velocity bounds can accomplish navigation tasks in unknown and dynamic environments without any retraining. This work greatly extends the application range of DRL-based navigation methods from the fixed dimensional configuration to varied dimensional configurations.

Includes members:
  Member "learning to adapt in dynamic real world environments through meta reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "revisiting meta learning as supervised learning")
  Member "soft actor critic algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ready policy one world building through active learning")
  Member "learning with stochastic guidance for navigation":
    also connected through other hyperedges:
      (none)
  Member "addressing function approximation error in actor critic methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "curiosity driven exploration for mapless navigation with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to walk in the real world with minimal human effort")
  Member "learning with training wheels speeding up training with a simple controller for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")

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)