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: "feasibility study moving non homogeneous teams in congested video game environments"
  Abstract: Multi-agent path finding (MAPF) is a well-studied problem in artificial intelligence, where one needs to find collision-free paths for agents with given start and goal locations. In video games, agents of different types often form teams. In this paper, we demonstrate the usefulness of MAPF algorithms from artificial intelligence for moving such non-homogeneous teams in congested video game environments.

Connected through hyperedge HE-1 (induced by source paper "multi agent pathfinding with continuous time"):
  with members:
    - "lifelong path planning with kinematic constraints for multi agent pickup and delivery"
    - "scalable asymptotically optimal multi robot motion planning"
    - "alan adaptive learning for multi agent navigation"
    - "multi agent path finding with delay probabilities"

Connected through hyperedge HE-2 (induced by source paper "multi agent path finding with deadlines preliminary results"):
  with members:
    - "overview a hierarchical framework for plan generation and execution in multirobot systems"
    - "overview generalizations of multi agent path finding to real world scenarios"
    - "lifelong multi agent path finding for online pickup and delivery tasks"
    - "optimal target assignment and path finding for teams of agents"
    - "multi agent path finding with delay probabilities"
    - "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 "searching with consistent prioritization for multi agent path finding"):
  with members:
    - "overview a hierarchical framework for plan generation and execution in multirobot systems"
    - "overview generalizations of multi agent path finding to real world scenarios"
    - "lifelong multi agent path finding for online pickup and delivery tasks"
    - "complete decentralized method for on line multi robot trajectory planning in valid infrastructures"
    - "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-4 (induced by source paper "multi agent path finding with deadlines"):
  with members:
    - "overview a hierarchical framework for plan generation and execution in multirobot systems"
    - "overview generalizations of multi agent path finding to real world scenarios"
    - "lifelong multi agent path finding for online pickup and delivery tasks"
    - "optimal target assignment and path finding for teams of agents"
    - "planning optimal paths for multiple robots on graphs"
    - "mapp a scalable multi agent path planning algorithm with tractability and completeness guarantees"

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)