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: "downwash aware trajectory planning for large quadrotor teams"
  Abstract: We describe a method for formation-change trajectory planning for large quadrotor teams in obstacle-rich environments. Our method decomposes the planning problem into two stages: a discrete planner operating on a graph representation of the workspace, and a continuous refinement that converts the non-smooth graph plan into a set of C^k-continuous trajectories, locally optimizing an integral-squared-derivative cost. We account for the downwash effect, allowing safe flight in dense formations. We demonstrate the computational efficiency in simulation with up to 200 robots and the physical plausibility with an experiment with 32 nano-quadrotors. Our approach can compute safe and smooth trajectories for hundreds of quadrotors in dense environments with obstacles in a few minutes.

Connected through hyperedge HE-1 (induced by source paper "multi agent motion planning for dense and dynamic environments via deep reinforcement learning"):
  with members:
    - "force based algorithm for motion planning of large agent teams"
    - "trajectory generation for multiagent point to point transitions via distributed model predictive control"
    - "pearl preference appraisal reinforcement learning for motion planning"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "socially aware motion planning with deep reinforcement learning"
    - "reinforcement learning through asynchronous advantage actor critic on a gpu"

Connected through hyperedge HE-2 (induced by source paper "a modular framework for motion planning using safe by design motion primitives"):
  with members:
    - "a framework for multi vehicle navigation using feedback based motion primitives"
    - "scalable asymptotically optimal multi robot motion planning"
    - "funnel libraries for real time robust feedback motion planning"
    - "an effective algorithmic framework for near optimal multi robot path planning"

Connected through hyperedge HE-3 (induced by source paper "force based algorithm for motion planning of large agent teams"):
  with members:
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "decentralized non communicating multiagent collision avoidance with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "multi vehicle trajectory optimisation on road networks"):
  with members:
    - "overview a hierarchical framework for plan generation and execution in multirobot systems"
    - "polling systems based autonomous vehicle coordination in traffic intersections with no traffic signals"
    - "multi vehicle cooperative control using mixed integer linear programming"

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)