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 "drones in distress a game theoretic countermeasure for protecting uavs against gps spoofing"):
  Abstract: One prominent security threat that targets unmanned aerial vehicles (UAVs) is the capture via GPS spoofing in which an attacker manipulates a UAV's global positioning system (GPS) signals in order to capture it. Given the anticipated widespread deployment of UAVs for various purposes, it is imperative to develop new security solutions against such attacks. In this paper, a mathematical framework is introduced for analyzing and mitigating the effects of GPS spoofing attacks on UAVs. In particular, system dynamics are used to model the optimal routes that the UAVs will adopt to reach their destinations. The GPS spoofer's effect on each UAV's route is also captured by the model. To this end, the spoofer's optimal imposed locations on the UAVs, are analytically derived; allowing the UAVs to predict their traveling routes under attack. Then, a countermeasure mechanism is developed to mitigate the effect of the GPS spoofing attack. The countermeasure is built on the premise of cooperative localization, in which a UAV can determine its location using nearby UAVs instead of the possibly compromised GPS locations. To better utilize the proposed defense mechanism, a dynamic Stackelberg game is formulated to model the interactions between a GPS spoofer and a drone operator. In particular, the drone operator acts as the leader that determines its optimal strategy in light of the spoofer's expected response strategy. The equilibrium strategies of the game are then analytically characterized and studied through a novel proposed algorithm. Simulation results show that, when combined with the Stackelberg strategies, the proposed defense mechanism will outperform baseline strategy selection techniques in terms of reducing the possibility of UAV capture

Includes members:
  Member "dynamic mobility aware interference avoidance for aerial base stations in cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "interference avoidance position planning in dual hop and multi hop uav relay networks")
  Member "a game of drones cyber physical security of time critical uav applications with cumulative prospect theory perceptions and valuations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on greedy and strategic evaders in sequential interdiction settings with incomplete information")
  Member "beyond 5g with uavs foundations of a 3d wireless cellular network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint 3d maneuver and power adaptation for secure uav communication with comp reception")
  Member "machine learning for wireless connectivity and security of cellular connected uavs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep reinforcement learning for minimizing age of information in uav assisted networks")
  Member "robust deep reinforcement learning for security and safety in autonomous vehicle systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "survey of deep reinforcement learning for motion planning of autonomous vehicles")
  Member "cyber physical security and safety of autonomous connected vehicles optimal control meets multi armed bandit learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an open source modeling framework for interdependent energy transportation communication infrastructure in smart and connected communities")
  Member "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed federated learning for ultra reliable low latency vehicular communications")
  Member "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the age of information in internet of things systems with correlated devices")

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.SY(Systems and Control)