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 "glas global to local safe autonomy synthesis for multi robot motion planning with end to end learning"):
  Abstract: We present GLAS: Global-to-Local Autonomy Synthesis, a provably-safe, automated distributed policy generation for multi-robot motion planning. Our approach combines the advantage of centralized planning of avoiding local minima with the advantage of decentralized controllers of scalability and distributed computation. In particular, our synthesized policies only require relative state information of nearby neighbors and obstacles, and compute a provably-safe action. Our approach has three major components: i) we generate demonstration trajectories using a global planner and extract local observations from them, ii) we use deep imitation learning to learn a decentralized policy that can run efficiently online, and iii) we introduce a novel differentiable safety module to ensure collision-free operation, enabling end-to-end policy training. Our numerical experiments demonstrate that our policies have a 20% higher success rate than ORCA across a wide range of robot and obstacle densities. We demonstrate our method on an aerial swarm, executing the policy on low-end microcontrollers in real-time.

Includes members:
  Member "trajectory generation for multiagent point to point transitions via distributed model predictive control":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi agent motion planning for dense and dynamic environments via deep reinforcement learning")
  Member "learning safe unlabeled multi robot planning with motion constraints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi vehicle mixed reality reinforcement learning for autonomous multi lane driving")
  Member "control regularization for reduced variance reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "regularization matters in policy optimization")
  Member "neural swarm decentralized close proximity multirotor controlusing learned interactions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "beyond no regret competitive control via online optimization with memory")
  Member "graph neural networks for decentralized multi robot path planning":
    also connected through other hyperedges:
      (none)
  Member "graph policy gradients for large scale unlabeled motion planning with constraints":
    also connected through other hyperedges:
      (none)
  Member "pytorch an imperative style high performance deep learning library":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "differentiable particle filters end to end learning with algorithmic priors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning task driven control policies via information bottlenecks")

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)