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 path planning with kinematic constraints for multi agent pickup and delivery"
  Abstract: The Multi-Agent Pickup and Delivery (MAPD) problem models applications where a large number of agents attend to a stream of incoming pickup-and-delivery tasks. Token Passing (TP) is a recent MAPD algorithm that is efficient and effective. We make TP even more efficient and effective by using a novel combinatorial search algorithm, called Safe Interval Path Planning with Reservation Table (SIPPwRT), for single-agent path planning. SIPPwRT uses an advanced data structure that allows for fast updates and lookups of the current paths of all agents in an online setting. The resulting MAPD algorithm TP-SIPPwRT takes kinematic constraints of real robots into account directly during planning, computes continuous agent movements with given velocities that work on non-holonomic robots rather than discrete agent movements with uniform velocity, and is complete for well-formed MAPD instances. We demonstrate its benefits for automated warehouses using both an agent simulator and a standard robot simulator. For example, we demonstrate that it can compute paths for hundreds of agents and thousands of tasks in seconds and is more efficient and effective than existing MAPD algorithms that use a post-processing step to adapt their paths to continuous agent movements with given velocities.

Connected through hyperedge HE-1 (induced by source paper "multi agent pathfinding definitions variants and benchmarks"):
  with members:
    - "come together multi agent geometric consensus gathering rendezvous clustering aggregation"
    - "multi agent path finding with deadlines"
    - "experimenting with robotic intra logistics domains"
    - "lifelong multi agent path finding for online pickup and delivery tasks"
    - "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-2 (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 multi agent path finding for online pickup and delivery tasks"
    - "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-3 (induced by source paper "multi agent pathfinding with continuous time"):
  with members:
    - "scalable asymptotically optimal multi robot motion planning"
    - "feasibility study moving non homogeneous teams in congested video game environments"
    - "alan adaptive learning for multi agent navigation"
    - "multi agent path finding with delay probabilities"

Connected through hyperedge HE-4 (induced by source paper "prioritized multi agent path finding for differential drive robots"):
  with members:
    - "searching with consistent prioritization for multi agent path finding"
    - "two techniques that enhance the performance of multi robot prioritized path planning"
    - "any angle pathfinding for multiple agents based on sipp algorithm"
    - "ai buzzwords explained multi agent path finding mapf"

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)