human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "multi agent pathfinding with continuous time"):
  Abstract: Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple agents such that every agent reaches its goal and the agents do not collide. Most prior work on MAPF was on grids, assumed agents' actions have uniform duration, and that time is discretized into timesteps. We propose a MAPF algorithm that does not rely on these assumptions, is complete, and provides provably optimal solutions. This algorithm is based on a novel adaptation of Safe interval path planning (SIPP), a continuous time single-agent planning algorithm, and a modified version of Conflict-based search (CBS), a state of the art multi-agent pathfinding algorithm. We analyze this algorithm, discuss its pros and cons, and evaluate it experimentally on several standard benchmarks.

Includes members:
  Member "lifelong path planning with kinematic constraints for multi agent pickup and delivery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi agent pathfinding definitions variants and benchmarks")
  Member "scalable asymptotically optimal multi robot motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "drrt scalable and informed asymptotically optimal multi robot motion planning")
  Member "feasibility study moving non homogeneous teams in congested video game environments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi agent path finding with deadlines preliminary results")
  Member "alan adaptive learning for multi agent navigation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reciprocal collision avoidance for general nonlinear agents using reinforcement learning")
  Member "multi agent path finding with delay probabilities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi agent pathfinding definitions variants and benchmarks")

Question: Please tell me which class the source paper 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)