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: "mapp a scalable multi agent path planning algorithm with tractability and completeness guarantees"
  Abstract: Multi-agent path planning is a challenging problem with numerous real-life applications. Running a centralized search such as A* in the combined state space of all units is complete and cost-optimal, but scales poorly, as the state space size is exponential in the number of mobile units. Traditional decentralized approaches, such as FAR and WHCA*, are faster and more scalable, being based on problem decomposition. However, such methods are incomplete and provide no guarantees with respect to the running time or the solution quality. They are not necessarily able to tell in a reasonable time whether they would succeed in finding a solution to a given instance.#R##N##R##N#We introduce MAPP, a tractable algorithm for multi-agent path planning on undirected graphs. We present a basic version and several extensions. They have low-polynomial worst-case upper bounds for the running time, the memory requirements, and the length of solutions. Even though all algorithmic versions are incomplete in the general case, each provides formal guarantees on problems it can solve. For each version, we discuss the algorithm's completeness with respect to clearly defined subclasses of instances.#R##N##R##N#Experiments were run on realistic game grid maps. MAPP solved 99.86% of all mobile units, which is 18-22% better than the percentage of FAR and WHCA*. MAPP marked 98.82% of all units as provably solvable during the first stage of plan computation. Parts of MAPP's computation can be re-used across instances on the same map. Speed-wise, MAPP is competitive or significantly faster than WHCA*, depending on whether MAPP performs all computations from scratch. When data that MAPP can re-use are preprocessed offline and readily available, MAPP is slower than the very fast FAR algorithm by a factor of 2.18 on average. MAPP's solutions are on average 20% longer than FAR's solutions and 7-31% longer than WHCA*'s solutions.

Connected through hyperedge HE-1 (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"
    - "lifelong multi agent path finding for online pickup and delivery tasks"
    - "learning the problem optimum map analysis and application to global optimization in robotics"

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 path planning with kinematic constraints for multi agent pickup and delivery"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "multi agent path finding with capacity constraints"):
  with members:
    - "multi agent path finding with deadlines"
    - "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"
    - "exploiting subgraph structure in multi robot path planning"

Connected through hyperedge HE-4 (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"
    - "multi agent path finding with deadlines"
    - "overview generalizations of multi agent path finding to real world scenarios"
    - "exploiting subgraph structure in multi robot path planning"

Connected through hyperedge HE-5 (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"
    - "multi agent path finding with deadlines"
    - "overview generalizations of multi agent path finding to real world scenarios"
    - "optimal multi robot path planning on graphs structure and computational complexity"
    - "exploiting subgraph structure in multi robot path planning"

Connected through hyperedge HE-6 (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"
    - "multi agent path finding with deadlines"
    - "pebble motion on graphs with rotations efficient feasibility tests and planning algorithms"

Connected through hyperedge HE-7 (induced by source paper "automatic algorithm selection in multi agent pathfinding"):
  with members:
    - "multi agent path finding with deadlines preliminary results"
    - "matconvnet convolutional neural networks for matlab"
    - "algorithm selection for combinatorial search problems a survey"
    - "planning optimal paths for multiple robots on graphs"

Connected through hyperedge HE-8 (induced by source paper "priority inheritance with backtracking for iterative multi agent path finding"):
  with members:
    - "searching with consistent prioritization for multi agent path finding"
    - "lifelong multi agent path finding for online pickup and delivery tasks"

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)