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: "trajectory generation for multiagent point to point transitions via distributed model predictive control"
  Abstract: This letter introduces a novel algorithm for multiagent offline trajectory generation based on distributed model predictive control. Central to the algorithm's scalability and success is the development of an on-demand collision avoidance strategy. By predicting future states and sharing this information with their neighbors, the agents are able to detect and avoid collisions while moving toward their goals. The proposed algorithm can be implemented in a distributed fashion and reduces the computation time by more than 85% compared to previous optimization approaches based on sequential convex programming, while only having a small impact on the optimality of the plans. The approach was validated both through extensive simulations and experimentally with teams of up to 25 quadrotors flying in confined indoor spaces.

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"
    - "pearl preference appraisal reinforcement learning for motion planning"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "downwash aware trajectory planning for large quadrotor teams"
    - "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 "glas global to local safe autonomy synthesis for multi robot motion planning with end to end learning"):
  with members:
    - "learning safe unlabeled multi robot planning with motion constraints"
    - "control regularization for reduced variance reinforcement learning"
    - "neural swarm decentralized close proximity multirotor controlusing learned interactions"
    - "graph neural networks for decentralized multi robot path planning"
    - "graph policy gradients for large scale unlabeled motion planning with constraints"
    - "pytorch an imperative style high performance deep learning library"
    - "differentiable particle filters end to end learning with algorithmic priors"

Connected through hyperedge HE-3 (induced by source paper "beyond reynolds a constraint driven approach to cluster flocking"):
  with members:
    - "constraint driven coordinated control of multi robot systems"

Connected through hyperedge HE-4 (induced by source paper "towards scalable continuous time trajectory optimization for multi robot navigation"):
  with members:
    - "online trajectory generation with distributed model predictive control for multi robot motion planning"
    - "safe aggressive quadrotor flight via reachability based trajectory design"
    - "continuous time trajectory optimization for decentralized multi robot navigation"
    - "safe certificate based maneuvers for teams of quadrotors using differential flatness"
    - "linear vs nonlinear mpc for trajectory tracking applied to rotary wing micro aerial vehicles"
    - "an effective algorithmic framework for near optimal multi robot path planning"
    - "finding a needle in an exponential haystack discrete rrt for exploration of implicit roadmaps in multi robot motion planning"

Connected through hyperedge HE-5 (induced by source paper "online trajectory generation with distributed model predictive control for multi robot motion planning"):
  with members:
    - "distributed model predictive control using a chain of tubes"

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)