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 "x anytime multi agent path finding for sparse domains using iterative repairs"):
  Abstract: Optimally solving the Multi-Agent Path Finding (MAPF) problem is computationally expensive; often, real world systems need a valid solution quickly and would like to improve the quality of the solution if time allows, making anytime MAPF solvers of great practical interest. In many of these practical domains, planning for each agent individually produces a solution with sparse agent-agent collisions involving few agents, and these collisions allow for a geometrically local repair, even if this repair is globally sub-optimal. Furthermore, if the geometric scope of the repair is increased, it will produce a higher quality repair; as the scope grows to the entire domain, it will produce a globally optimal repair. Using these properties, we present an anytime MAPF framework, WAMPF, along with an efficient implementation, X*, which employs re-use techniques during repair scope growth. On the theoretical side, we provide full proofs of correctness for WAMPF and X*. On the experimental side, we show that X* outperforms state-of-the-art anytime or optimal MAPF solvers in fast solution generation and is competitive with state-of-the-art anytime or optimal MAPF solvers for optimal solution generation.

Includes members:
  Member "planning optimal paths for multiple robots on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "drrt scalable and informed asymptotically optimal multi robot motion planning")
  Member "sampling based algorithms for optimal motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "exploiting subgraph structure in multi robot path planning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi agent path finding with capacity constraints")

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.MA(Multiagent Systems)