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: "optimal target assignment and path finding for teams of agents"
  Abstract: We study the TAPF (combined target-assignment and path-finding) problem for teams of agents in known terrain, which generalizes both the anonymous and non-anonymous multi-agent path-finding problems. Each of the teams is given the same number of targets as there are agents in the team. Each agent has to move to exactly one target given to its team such that all targets are visited. The TAPF problem is to first assign agents to targets and then plan collision-free paths for the agents to their targets in a way such that the makespan is minimized. We present the CBM (Conflict-Based Min-Cost-Flow) algorithm, a hierarchical algorithm that solves TAPF instances optimally by combining ideas from anonymous and non-anonymous multi-agent path-finding algorithms. On the low level, CBM uses a min-cost max-flow algorithm on a time-expanded network to assign all agents in a single team to targets and plan their paths. On the high level, CBM uses conflict-based search to resolve collisions among agents in different teams. Theoretically, we prove that CBM is correct, complete and optimal. Experimentally, we show the scalability of CBM to TAPF instances with dozens of teams and hundreds of agents and adapt it to a simulated warehouse system.

Connected through hyperedge HE-1 (induced by source paper "a job assignment heuristic for lifelong multi agent path finding problem with multiple delivery locations"):
  with members:
    - "overview generalizations of multi agent path finding to real world scenarios"
    - "lifelong multi agent path finding for online pickup and delivery tasks"

Connected through hyperedge HE-2 (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"
    - "multi agent path finding with deadlines"
    - "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"
    - "multi agent path finding with delay probabilities"

Connected through hyperedge HE-3 (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"
    - "multi agent path planning and network flow"
    - "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 "strata a unified framework for task assignments in large teams of heterogeneous robots"):
  with members:
    - "anonymous hedonic game for task allocation in a large scale multiple agent system"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "local information based control for probabilistic swarm distribution guidance"

Connected through hyperedge HE-5 (induced by source paper "an optimal algorithm to solve the combined task allocation and path finding problem"):
  with members:
    - "lifelong multi agent path finding for online pickup and delivery tasks"
    - "provably safe and deadlock free execution of multi robot plans under delaying disturbances"

Connected through hyperedge HE-6 (induced by source paper "a constraint programming approach to simultaneous task allocation and motion scheduling for industrial dual arm manipulation tasks"):
  with members:
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "overview generalizations of multi agent path finding to real world scenarios"

Connected through hyperedge HE-7 (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"
    - "feasibility study moving non homogeneous teams in congested video game environments"
    - "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-8 (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"
    - "feasibility study moving non homogeneous teams in congested video game environments"
    - "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)