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: "cost benefit analysis of moving target defense in power grids"
  Abstract: We study moving-target defense (MTD) that actively perturbs transmission line reactances to thwart stealthy false data injection (FDI) attacks against state estimation in a power grid. Prior work on this topic has proposed MTD based on randomly selected reactance perturbations, but these perturbations cannot guarantee effective attack detection. To address the issue, we present formal design criteria to select MTD reactance perturbations that are truly effective. However, based on a key optimal power flow (OPF) formulation, we find that the effective MTD may incur a non-trivial operational cost that has not hitherto received attention. Accordingly, we characterize important tradeoffs between the MTD's detection capability and its associated required cost. Extensive simulations, using the MATPOWER simulator and benchmark IEEE bus systems, verify and illustrate the proposed design approach that for the first time addresses both key aspects of cost and effectiveness of the MTD.

Connected through hyperedge HE-1 (induced by source paper "toward proactive adaptive defense a survey on moving target defense"):
  with members:
    - "modeling and analysis of integrated proactive defense mechanisms for internet of things"
    - "moving target defense for deep visual sensing against adversarial examples"
    - "defending via strategic ml selection"
    - "a survey of moving target defenses for network security"
    - "frvm flexible random virtual ip multiplexing in software defined networks"
    - "markov game modeling of moving target defense for strategic detection of threats in cloud networks"
    - "mtdeep boosting the security of deep neural nets against adversarial attacks with moving target defense"
    - "a game theoretic taxonomy and survey of defensive deception for cybersecurity and privacy"

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.CR(Cryptography and Security)