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: "on greedy routing in dynamic uav networks"
  Abstract: Unmanned aerial vehicles (UAVs), commonly known as drones, are becoming increasingly popular for various applications. Freely flying drones create highly dynamic environments, where conventional routing algorithms which rely on stationary network contact graphs fail to perform efficiently. Also, link establishment through exploring optimal paths using hello messages (as is used in AODV algorithm) deems extremely inefficient and costly for rapidly changing network topologies. #R##N#In this paper, we present a distance-based greedy routing algorithm for UAV networks solely based on UAVs' local observations of their surrounding subnetwork. Thereby, neither a central decision maker nor a time-consuming route setup and maintenance mechanism are required. To evaluate the proposed method, we derive an analytical bound for the expected number of hops that a packet traverses. Also, we find the expected end-to-end distance traveled by each packet as well as the probability of successful delivery. The simulation results verify the accuracy of the developed analytical expressions and show considerable improvement compared to centralized shortest path routing algorithms.

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"
    - "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 "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"
    - "leader follower based coalition formation in large scale uav networks a quantum evolutionary approach"
    - "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-3 (induced by source paper "a unified framework for joint mobility prediction and object profiling of drones in uav networks"):
  with members:
    - "dronecells improving 5g spectral efficiency using drone mounted flying base stations"
    - "optimal measurement policy for predicting uav network topology"
    - "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-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"
    - "leader follower based coalition formation in large scale uav networks a quantum evolutionary approach"
    - "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"

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.NI(Networking and Internet Architecture)