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 "leader follower based coalition formation in large scale uav networks a quantum evolutionary approach"):
  Abstract: The problem of decentralized multiple Point of Interests (PoIs) detection and associated task completion in an unknown environment with multiple resource-constrained and self-interested Unmanned Aerial Vehicles (UAVs) is studied. The UAVs form several coalitions to efficiently complete the compound tasks which are impossible to be performed individually. The objectives of such coalition formation are to firstly minimize resource consumption in completing the encountered tasks on time, secondly to enhance the reliability of the coalitions, and lastly in segregating the most trusted UAVs amid the self interested of them. As many previous publications have merely focused on minimizing costs, this study considers a multi-objective optimization coalition formation problem that considers the three aforementioned objectives. In doing so, a leader-follower- inspired coalition formation algorithm amalgamating the three objectives to address the problem of the computational complexity of coalition formation in large-scale UAV networks is proposed. This algorithm attempts to form the coalitions with minimally exceeding the required resources for the encountered tasks while maximizing the number of completed tasks. The proposed algorithm is based on Quantum Evolutionary Algorithms(QEA) which are a combination of quantum computing and evolutionary algorithms. Results from simulations show that the proposed algorithm significantly outperforms the existing coalition formation algorithms such as merge-and-split and a famous multi-objective genetic algorithm called NSGA-II.

Includes members:
  Member "optimal measurement policy for predicting uav network topology":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wildfire monitoring in remote areas using autonomous unmanned aerial vehicles")
  Member "a coalition formation approach to coordinated task allocation in heterogeneous uav networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "wildfire monitoring in remote areas using autonomous unmanned aerial vehicles")
  Member "autonomous extracting a hierarchical structure of tasks in reinforcement learning and multi task reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "meta reinforcement learning with autonomous inference of subtask dependencies")

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)