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: "lifelong multi agent path finding for online pickup and delivery tasks"
  Abstract: The multi-agent path-finding (MAPF) problem has recently received a lot of attention. However, it does not capture important characteristics of many real-world domains, such as automated warehouses, where agents are constantly engaged with new tasks. In this paper, we therefore study a lifelong version of the MAPF problem, called the multi-agent pickup and delivery (MAPD) problem. In the MAPD problem, agents have to attend to a stream of delivery tasks in an online setting. One agent has to be assigned to each delivery task. This agent has to first move to a given pickup location and then to a given delivery location while avoiding collisions with other agents. We present two decoupled MAPD algorithms, Token Passing (TP) and Token Passing with Task Swaps (TPTS). Theoretically, we show that they solve all well-formed MAPD instances, a realistic subclass of MAPD instances. Experimentally, we compare them against a centralized strawman MAPD algorithm without this guarantee in a simulated warehouse system. TP can easily be extended to a fully distributed MAPD algorithm and is the best choice when real-time computation is of primary concern since it remains efficient for MAPD instances with hundreds of agents and tasks. TPTS requires limited communication among agents and balances well between TP and the centralized MAPD algorithm.

Connected through hyperedge HE-1 (induced by source paper "efficient multi agent motion planning in continuous workspaces using medial axis based swap graphs"):
  with members:
    - "the starcraft multi agent challenge"
    - "sear a polynomial time multi robot path planning algorithm with expected constant factor optimality guarantee"
    - "average case constant factor time and distance optimal multi robot path planning in well connected environments"
    - "motion planning for unlabeled discs with optimality guarantees"
    - "optimal multi robot path planning on graphs structure and computational complexity"
    - "pebble motion on graphs with rotations efficient feasibility tests and planning algorithms"

Connected through hyperedge HE-2 (induced by source paper "a job assignment heuristic for lifelong multi agent path finding problem with multiple delivery locations"):
  with members:
    - "overview generalizations of multi agent path finding to real world scenarios"
    - "optimal target assignment and path finding for teams of agents"

Connected through hyperedge HE-3 (induced by source paper "ddm fast near optimal multi robot path planning using diversified path and optimal sub problem solution database heuristics"):
  with members:
    - "multi agent pathfinding definitions variants and benchmarks"
    - "priority inheritance with backtracking for iterative multi agent path finding"
    - "automatic algorithm selection in multi agent pathfinding"
    - "coordinating the motion of labeled discs with optimality guarantees under extreme density"
    - "x anytime multiagent planning with bounded search"
    - "constant factor time optimal multi robot routing on high dimensional grids in mostly sub quadratic time"
    - "learning the problem optimum map analysis and application to global optimization in robotics"
    - "motion planning for unlabeled discs with optimality guarantees"

Connected through hyperedge HE-4 (induced by source paper "multi agent pathfinding definitions variants and benchmarks"):
  with members:
    - "come together multi agent geometric consensus gathering rendezvous clustering aggregation"
    - "lifelong path planning with kinematic constraints for multi agent pickup and delivery"
    - "multi agent path finding with deadlines"
    - "experimenting with robotic intra logistics domains"
    - "any angle pathfinding for multiple agents based on sipp algorithm"
    - "ai buzzwords explained multi agent path finding mapf"
    - "optimal target assignment and path finding for teams of agents"
    - "multi agent path finding with delay probabilities"

Connected through hyperedge HE-5 (induced by source paper "idle time optimization for target assignment and path finding in sortation centers"):
  with members:
    - "multi agent pathfinding definitions variants and benchmarks"
    - "searching with consistent prioritization for multi agent path finding"
    - "lifelong path planning with kinematic constraints for multi agent pickup and delivery"
    - "ai buzzwords explained multi agent path finding mapf"
    - "optimal target assignment and path finding for teams of agents"
    - "multi agent path planning and network flow"
    - "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-6 (induced by source paper "efficient large scale multi drone delivery using transit networks"):
  with members:
    - "on the interaction between autonomous mobility on demand and public transportation systems"
    - "a bcmp network approach to modeling and controlling autonomous mobility on demand systems"
    - "route planning in transportation networks"

Connected through hyperedge HE-7 (induced by source paper "winpibt expanded prioritized algorithm for iterative multi agent path finding"):
  with members:
    - "priority inheritance with backtracking for iterative multi agent path finding"
    - "searching with consistent prioritization for multi agent path finding"

Connected through hyperedge HE-8 (induced by source paper "an optimal algorithm to solve the combined task allocation and path finding problem"):
  with members:
    - "optimal target assignment and path finding for teams of agents"
    - "provably safe and deadlock free execution of multi robot plans under delaying disturbances"

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)