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 "cyber physical security and safety of autonomous connected vehicles optimal control meets multi armed bandit learning"):
  Abstract: Autonomous connected vehicles (ACVs) rely on intra-vehicle sensors such as camera and radar as well as inter-vehicle communication to operate effectively. This reliance on cyber components exposes ACVs to cyber and physical attacks in which an adversary can manipulate sensor readings and physically take control of an ACV. In this paper, a comprehensive framework is proposed to thwart cyber and physical attacks on ACV networks. First, an optimal safe controller for ACVs is derived to maximize the street traffic flow while minimizing the risk of accidents by optimizing ACV speed and inter-ACV spacing. It is proven that the proposed controller is robust to physical attacks which aim at making ACV systems instable. To improve the cyber-physical security of ACV systems, next, data injection attack (DIA) detection approaches are proposed to address cyber attacks on sensors and their physical impact on the ACV system. To comprehensively design the DIA detection approaches, ACV sensors are characterized in two subsets based on the availability of a-priori information about their data. For sensors having a prior information, a DIA detection approach is proposed and an optimal threshold level is derived for the difference between the actual and estimated values of sensors data which enables ACV to stay robust against cyber attacks. For sensors having no prior information, a novel multi-armed bandit (MAB) algorithm is proposed to enable ACV to securely control its motion. Simulation results show that the proposed optimal safe controller outperforms current state of the art controllers by maximizing the robustness of ACVs to physical attacks. The results also show that the proposed DIA detection approaches, compared to Kalman filtering, can improve the security of ACV sensors against cyber attacks and ultimately improve the physical robustness of an ACV system.

Includes members:
  Member "sleeping multi armed bandit learning for fast uplink grant allocation in machine type communications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "contextual bandit learning for machine type communications in the null space of multi antenna systems")
  Member "robust deep reinforcement learning for security and safety in autonomous vehicle systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "survey of deep reinforcement learning for motion planning of autonomous vehicles")
  Member "joint communication and control for wireless autonomous vehicular platoon systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimized age of information tail for ultra reliable low latency communications in vehicular networks")
  Member "stochastic optimization and control framework for 5g network slicing with effective isolation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "energy efficient resource allocation in uav enabled mobile edge computing networks")
  Member "deep learning for signal authentication and security in massive internet of things systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial deep learning for over the air spectrum poisoning attacks")
  Member "deep learning for reliable mobile edge analytics in intelligent transportation systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal time allocation in vanets advertising a price based approach using stackelberg game")
  Member "a colonel blotto game for interdependence aware cyber physical systems security in smart cities":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "flipin a game theoretic cyber insurance framework for incentive compatible cyber risk management of internet of things")
  Member "a survey on low latency towards 5g ran core network and caching solutions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the design of dynamic probabilistic caching with time varying content popularity")

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)