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: "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.

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.RO(Robotics)