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 "multi vehicle mixed reality reinforcement learning for autonomous multi lane driving"):
  Abstract: Autonomous driving promises to transform road transport. Multi-vehicle and multi-lane scenarios, however, present unique challenges due to constrained navigation and unpredictable vehicle interactions. Learning-based methods---such as deep reinforcement learning---are emerging as a promising approach to automatically design intelligent driving policies that can cope with these challenges. Yet, the process of safely learning multi-vehicle driving behaviours is hard: while collisions---and their near-avoidance---are essential to the learning process, directly executing immature policies on autonomous vehicles raises considerable safety concerns. In this article, we present a safe and efficient framework that enables the learning of driving policies for autonomous vehicles operating in a shared workspace, where the absence of collisions cannot be guaranteed. Key to our learning procedure is a sim2real approach that uses real-world online policy adaptation in a mixed-reality setup, where other vehicles and static obstacles exist in the virtual domain. This allows us to perform safe learning by simulating (and learning from) collisions between the learning agent(s) and other objects in virtual reality. Our results demonstrate that, after only a few runs in mixed-reality, collisions are significantly reduced.

Includes members:
  Member "a fleet of miniature cars for experiments in cooperative driving":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi robot path deconfliction through prioritization by path prospects")
  Member "learning safe unlabeled multi robot planning with motion constraints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "glas global to local safe autonomy synthesis for multi robot motion planning with end to end learning")
  Member "sim to multi real transfer of low level robust control policies to multiple quadrotors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to seek deep reinforcement learning for phototaxis of a nano drone in an obstacle field")
  Member "deepracer educational autonomous racing platform for experimentation with sim2real reinforcement learning":
    also connected through other hyperedges:
      (none)
  Member "sim to real transfer of robotic control with dynamics randomization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "sim to real learning agile locomotion for quadruped robots":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to fly via deep model based reinforcement learning")
  Member "learning to adapt in dynamic real world environments through meta reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "revisiting meta learning as supervised learning")
  Member "closing the sim to real loop adapting simulation randomization with real world experience":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to fly via deep model based reinforcement learning")

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)