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 "a modular framework for motion planning using safe by design motion primitives"):
  Abstract: We present a modular framework for solving a motion planning problem among a group of robots. The proposed framework utilizes a finite set of low level motion primitives to generate motions in a gridded workspace. The constraints on allowable sequences of motion primitives are formalized through a maneuver automaton. At the high level, a control policy determines which motion primitive is executed in each box of the gridded workspace. We state general conditions on motion primitives to obtain provably correct behavior so that a library of safe-by-design motion primitives can be designed. The overall framework yields a highly robust design by utilizing feedback strategies at both the low and high levels. We provide specific designs for motion primitives and control policies suitable for multi-robot motion planning; the modularity of our approach enables one to independently customize the designs of each of these components. Our approach is experimentally validated on a group of quadrocopters.

Includes members:
  Member "a framework for multi vehicle navigation using feedback based motion primitives":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hierarchically consistent motion primitives for quadrotor coordination")
  Member "scalable asymptotically optimal multi robot motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "drrt scalable and informed asymptotically optimal multi robot motion planning")
  Member "downwash aware trajectory planning for large quadrotor teams":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi agent motion planning for dense and dynamic environments via deep reinforcement learning")
  Member "funnel libraries for real time robust feedback motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probably approximately correct vision based planning using motion primitives")
  Member "an effective algorithmic framework for near optimal multi robot path planning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepmnavigate deep reinforced multi robot navigation unifying local global collision avoidance")

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)