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: "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.

Connected through hyperedge HE-1 (induced by source paper "wildfire monitoring in remote areas using autonomous unmanned aerial vehicles"):
  with members:
    - "ring oscillator and its application as physical unclonable function puf for password management"
    - "overview of blakley s secret sharing scheme"
    - "distributed cooperative spectrum sharing in uav networks using multi agent reinforcement learning"
    - "autonomous extraction of a hierarchical structure of tasks in reinforcement learning a sequential associate rule mining approach"
    - "a reputation based stackelberg game model to enhance secrecy rate in spectrum leasing to selfish iot devices"
    - "on greedy routing in dynamic uav networks"
    - "a unified framework for joint mobility prediction and object profiling of drones in uav networks"
    - "optimal measurement policy for predicting uav network topology"

Connected through hyperedge HE-2 (induced by source paper "an autonomous spectrum management scheme for unmanned aerial vehicle networks in disaster relief operations"):
  with members:
    - "a solution for dynamic spectrum management in mission critical uav networks"
    - "a survey on hardware based security mechanisms for internet of things"
    - "ring oscillator and its application as physical unclonable function puf for password management"
    - "overview of blakley s secret sharing scheme"
    - "a reputation based stackelberg game model to enhance secrecy rate in spectrum leasing to selfish iot devices"
    - "traffic light control using deep policy gradient and value function based reinforcement learning"
    - "waveform and spectrum management for unmanned aerial systems beyond 2025"
    - "spectrum sharing and cyclical multiple access in uav aided cellular offloading"

Connected through hyperedge HE-3 (induced by source paper "a solution for dynamic spectrum management in mission critical uav networks"):
  with members:
    - "ring oscillator and its application as physical unclonable function puf for password management"
    - "overview of blakley s secret sharing scheme"
    - "distributed cooperative spectrum sharing in uav networks using multi agent reinforcement learning"
    - "a reputation based stackelberg game model to enhance secrecy rate in spectrum leasing to selfish iot devices"
    - "on greedy routing in dynamic uav networks"
    - "traffic light control using deep policy gradient and value function based reinforcement learning"
    - "deep reinforcement learning an overview"
    - "learning to predict where to look in interactive environments using deep recurrent q learning"

Connected through hyperedge HE-4 (induced by source paper "distributed cooperative spectrum sharing in uav networks using multi agent reinforcement learning"):
  with members:
    - "a reputation based stackelberg game model to enhance secrecy rate in spectrum leasing to selfish iot devices"
    - "on greedy routing in dynamic uav networks"
    - "a unified framework for joint mobility prediction and object profiling of drones in uav networks"
    - "optimal measurement policy for predicting uav network topology"
    - "a coalition formation approach to coordinated task allocation in heterogeneous uav networks"
    - "deep reinforcement learning an overview"
    - "optimal transport theory for power efficient deployment of unmanned aerial vehicles"
    - "spectrum sharing in cooperative cognitive radio networks a matching game framework"

Connected through hyperedge HE-5 (induced by source paper "on greedy routing in dynamic uav networks"):
  with members:
    - "optimal measurement policy for predicting uav network topology"
    - "dynamic routing for flying ad hoc networks"

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)