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: "distributed cooperative spectrum sharing in uav networks using multi agent reinforcement learning"
  Abstract: In this paper, we develop a distributed mechanism for spectrum sharing among a network of unmanned aerial vehicles (UAV) and licensed terrestrial networks. This method can provide a practical solution for situations where the UAV network may need external spectrum when dealing with congested spectrum or need to change its operating frequency due to security threats. Here we study a scenario where the UAV network performs a remote sensing mission. In this model, the UAVs are categorized into two clusters of relaying and sensing UAVs. The relay UAVs provide a relaying service for a licensed network to obtain spectrum access for the rest of UAVs that perform the sensing task. We develop a distributed mechanism in which the UAVs locally decide whether they need to participate in relaying or sensing considering the fact that communications among UAVs may not be feasible or reliable. The UAVs learn the optimal task allocation using a distributed reinforcement learning algorithm. Convergence of the algorithm is discussed and simulation results are presented for different scenarios to verify the convergence.

Connected through hyperedge HE-1 (induced by source paper "a survey on hardware based security mechanisms for internet of things"):
  with members:
    - "a solution for dynamic spectrum management in mission critical uav networks"
    - "a proof of concept sram based physically unclonable function puf key generation mechanism for iot devices"
    - "ring oscillator and its application as physical unclonable function puf for password management"
    - "overview of blakley s secret sharing scheme"
    - "internet of things survey on security"
    - "a reputation based stackelberg game model to enhance secrecy rate in spectrum leasing to selfish iot devices"
    - "a secret key generation scheme for internet of things using ternary states reram based physical unclonable functions"
    - "early warnings of cyber threats in online discussions"

Connected through hyperedge HE-2 (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"
    - "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"
    - "leader follower based coalition formation in large scale uav networks a quantum evolutionary approach"
    - "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-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"
    - "a reputation based stackelberg game model to enhance secrecy rate in spectrum leasing to selfish iot devices"
    - "leader follower based coalition formation in large scale uav networks a quantum evolutionary approach"
    - "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"

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)