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: "job selection in a network of autonomous uavs for delivery of goods"
  Abstract: This article analyzes two classes of job selection policies that control how a network of autonomous aerial vehicles delivers goods from depots to customers. Customer requests (jobs) occur according to a spatio-temporal stochastic process not known by the system. If job selection uses a policy in which the first job (FJ) is served first, the system may collapse to instability by removing just one vehicle. Policies that serve the nearest job (NJ) first show such threshold behavior only in some settings and can be implemented in a distributed manner. The timing of job selection has significant impact on delivery time and stability for NJ while it has no impact for FJ. Based on these findings we introduce a methodological approach for decision- making support to set up and operate such a system, taking into account the trade-off between monetary cost and service quality. In particular, we compute a lower bound for the infrastructure expenditure required to achieve a certain expected delivery time. The approach includes three time horizons: long-term decisions on the number of depots to deploy in the service area, mid- term decisions on the number of vehicles to use, and short-term decisions on the policy to operate the vehicles.

Connected through hyperedge HE-1 (induced by source paper "graph neural networks for decentralized multi robot path planning"):
  with members:
    - "multi robot path deconfliction through prioritization by path prospects"
    - "learning decentralized controllers for robot swarms with graph neural networks"
    - "stability properties of graph neural networks"
    - "primal pathfinding via reinforcement and imitation multi agent learning"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "graph neural networks for learning robot team coordination"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-2 (induced by source paper "multi robot path deconfliction through prioritization by path prospects"):
  with members:
    - "a fleet of miniature cars for experiments in cooperative driving"
    - "searching with consistent prioritization for multi agent path finding"

Connected through hyperedge HE-3 (induced by source paper "deep q learning for same day delivery with a heterogeneous fleet of vehicles and drones"):
  with members:
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "machine learning for wireless connectivity and security of cellular connected uavs"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "mobility support for cellular connected unmanned aerial vehicles performance and analysis"
    - "securing uav communications via joint trajectory and power control"
    - "deep learning for signal authentication and security in massive internet of things systems"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"
    - "proactive resource management for lte in unlicensed spectrum a deep learning perspective"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"

Connected through hyperedge HE-5 (induced by source paper "resilient assignment using redundant robots on transport networks with uncertain travel time"):
  with members:
    - "redundant robot assignment on graphs with uncertain edge costs"
    - "privacy preserving vehicle assignment for mobility on demand systems"
    - "a supermodular optimization framework for leader selection under link noise in linear multi agent systems"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "efficient informative sensing using multiple robots"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-6 (induced by source paper "redundant robot assignment on graphs with uncertain edge costs"):
  with members:
    - "a supermodular optimization framework for leader selection under link noise in linear multi agent systems"
    - "efficient informative sensing using multiple robots"
    - "combinatorial auctions with decreasing marginal utilities"

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.MA(Multiagent Systems)