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: "overview a hierarchical framework for plan generation and execution in multirobot systems"
  Abstract: The authors present an overview of a hierarchical framework for coordinating task- and motion-level operations in multirobot systems. Their framework is based on the idea of using simple temporal networks to simultaneously reason about precedence/causal constraints required for task-level coordination and simple temporal constraints required to take some kinematic constraints of robots into account. In the plan-generation phase, the framework provides a computationally scalable method for generating plans that achieve high-level tasks for groups of robots and take some of their kinematic constraints into account. In the plan-execution phase, the framework provides a method for absorbing an imperfect plan execution to avoid time-consuming re-planning in many cases. The authors use the multirobot path-planning problem as a case study to present the key ideas behind their framework for the long-term autonomy of multirobot systems.

Connected through hyperedge HE-1 (induced by source paper "hierarchical reinforcement learning framework towards multi agent navigation"):
  with members:
    - "learning and inferring a driver s braking action in car following scenarios"
    - "understanding human behaviors in crowds by imitating the decision making process"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "extracting traffic primitives directly from naturalistically logged data for self driving applications"
    - "hindsight experience replay"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "multi agent path finding with deadlines preliminary results"):
  with members:
    - "overview generalizations of multi agent path finding to real world scenarios"
    - "lifelong multi agent path finding for online pickup and delivery tasks"
    - "feasibility study moving non homogeneous teams in congested video game environments"
    - "optimal target assignment and path finding for teams of agents"
    - "multi agent path finding with delay probabilities"
    - "planning optimal paths for multiple robots on graphs"
    - "mapp a scalable multi agent path planning algorithm with tractability and completeness guarantees"

Connected through hyperedge HE-3 (induced by source paper "searching with consistent prioritization for multi agent path finding"):
  with members:
    - "overview generalizations of multi agent path finding to real world scenarios"
    - "lifelong multi agent path finding for online pickup and delivery tasks"
    - "feasibility study moving non homogeneous teams in congested video game environments"
    - "complete decentralized method for on line multi robot trajectory planning in valid infrastructures"
    - "planning optimal paths for multiple robots on graphs"
    - "mapp a scalable multi agent path planning algorithm with tractability and completeness guarantees"

Connected through hyperedge HE-4 (induced by source paper "multi agent path finding with deadlines"):
  with members:
    - "overview generalizations of multi agent path finding to real world scenarios"
    - "lifelong multi agent path finding for online pickup and delivery tasks"
    - "feasibility study moving non homogeneous teams in congested video game environments"
    - "optimal target assignment and path finding for teams of agents"
    - "planning optimal paths for multiple robots on graphs"
    - "mapp a scalable multi agent path planning algorithm with tractability and completeness guarantees"

Connected through hyperedge HE-5 (induced by source paper "multi vehicle trajectory optimisation on road networks"):
  with members:
    - "downwash aware trajectory planning for large quadrotor teams"
    - "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.AI(Artificial Intelligence)