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: "multi agent path finding with deadlines"
  Abstract: We formalize Multi-Agent Path Finding with Deadlines (MAPF-DL). The objective is to maximize the number of agents that can reach their given goal vertices from their given start vertices within the deadline, without colliding with each other. We first show that MAPF-DL is NP-hard to solve optimally. We then present two classes of optimal algorithms, one based on a reduction of MAPF-DL to a flow problem and a subsequent compact integer linear programming formulation of the resulting reduced abstracted multi-commodity flow network and the other one based on novel combinatorial search algorithms. Our empirical results demonstrate that these MAPF-DL solvers scale well and each one dominates the other ones in different scenarios.

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"
    - "lifelong path planning with kinematic constraints for multi agent pickup and delivery"
    - "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 "multi agent path finding with capacity constraints"):
  with members:
    - "lazy modeling of variants of token swapping problem and multi agent path finding through combination of satisfiability modulo theories and conflict based search"
    - "towards search based motion planning for micro aerial vehicles"
    - "overview generalizations of multi agent path finding to real world scenarios"
    - "mapp a scalable multi agent path planning algorithm with tractability and completeness guarantees"
    - "exploiting subgraph structure in multi robot path planning"

Connected through hyperedge HE-3 (induced by source paper "on the tour towards dpll mapf and beyond"):
  with members:
    - "multi agent pathfinding mapf with continuous time"
    - "multi agent path finding with continuous time viewed through satisfiability modulo theories smt"
    - "overview generalizations of multi agent path finding to real world scenarios"
    - "mapp a scalable multi agent path planning algorithm with tractability and completeness guarantees"
    - "exploiting subgraph structure in multi robot path planning"

Connected through hyperedge HE-4 (induced by source paper "multi agent path finding with continuous time viewed through satisfiability modulo theories smt"):
  with members:
    - "multi agent pathfinding mapf with continuous time"
    - "overview generalizations of multi agent path finding to real world scenarios"
    - "optimal multi robot path planning on graphs structure and computational complexity"
    - "mapp a scalable multi agent path planning algorithm with tractability and completeness guarantees"
    - "exploiting subgraph structure in multi robot path planning"

Connected through hyperedge HE-5 (induced by source paper "multi robot path planning via genetic programming"):
  with members:
    - "coordinating the motion of labeled discs with optimality guarantees under extreme density"
    - "sear a polynomial time multi robot path planning algorithm with expected constant factor optimality guarantee"
    - "pebble motion on graphs with rotations efficient feasibility tests and planning algorithms"
    - "mapp a scalable multi agent path planning algorithm with tractability and completeness guarantees"

Connected through hyperedge HE-6 (induced by source paper "integer programming as a general solution methodology for path based optimization in robotics principles best practices and applications"):
  with members:
    - "complexity results and fast methods for optimal tabletop rearrangement with overhand grasps"
    - "optimal tourist problem and anytime planning of trip itineraries"
    - "incremental sampling based algorithm for minimum violation motion planning"
    - "k color multi robot motion planning"

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)