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 measurement policy for predicting uav network topology"
  Abstract: In recent years, there has been a growing interest in using networks of Unmanned Aerial Vehicles (UAV) that collectively perform complex tasks for diverse applications. An important challenge in realizing UAV networks is the need for a communication platform that accommodates rapid network topology changes. For instance, a timely prediction of network topology changes can reduce communication link loss rate by setting up links with prolonged connectivity. #R##N#In this work, we develop an optimal tracking policy for each UAV to perceive its surrounding network configuration in order to facilitate more efficient communication protocols. More specifically, we develop an algorithm based on particle swarm optimization and Kalman filtering with intermittent observations to find a set of optimal tracking policies for each UAV under time-varying channel qualities and constrained tracking resources such that the overall network estimation error is minimized.

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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a unified framework for joint mobility prediction and object profiling of drones in uav networks"):
  with members:
    - "on greedy routing in dynamic uav networks"
    - "dronecells improving 5g spectral efficiency using drone mounted flying base stations"
    - "a coalition formation approach to coordinated task allocation in heterogeneous uav networks"
    - "amateur drone monitoring state of the art architectures key enabling technologies and future research directions"
    - "survey of important issues in uav communication networks"
    - "dynamic routing for flying ad hoc networks"
    - "internet of things applications animal monitoring with unmanned aerial vehicle"
    - "machine learning in wireless sensor networks algorithms strategies and applications"

Connected through hyperedge HE-3 (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"
    - "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"
    - "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-4 (induced by source paper "leader follower based coalition formation in large scale uav networks a quantum evolutionary approach"):
  with members:
    - "a coalition formation approach to coordinated task allocation in heterogeneous uav networks"
    - "autonomous extracting a hierarchical structure of tasks in reinforcement learning and multi task reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "on greedy routing in dynamic uav networks"):
  with members:
    - "leader follower based coalition formation in large scale uav networks a quantum evolutionary approach"
    - "dynamic routing for flying ad hoc networks"

Connected through hyperedge HE-6 (induced by source paper "a coalition formation approach to coordinated task allocation in heterogeneous uav networks"):
  with members:
    - "spectrum sharing in cooperative cognitive radio networks a matching game framework"
    - "a generic approach to coalition formation"

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.SY(Systems and Control)