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: "a survey of moving target defenses for network security"
  Abstract: Network defense techniques based on traditional tools, techniques, and procedures fail to account for the attacker's inherent advantage present due to the static nature of network services and configurations. Moving Target Defense (MTD), on the other hand, provides an intelligent countermeasure by dynamically re-configuring the underlying systems, thereby reducing the effectiveness of cyber attacks. In this survey, we analyze the recent advancements made in the development of MTDs and highlight how these defenses can be made more effective with the use of artificial intelligence techniques for decision making. We first define a unified formal notation for MTDs that can capture different aspects of such defenses. We then categorize these defenses into different sub-classes depending on how they answer the three questions -- what to move, when to move and how to move -- showcasing how game-theoretic strategies can effectively answer the latter question. To understand the usefulness of these defense methods, we study the implementation of such MTD techniques. We find that new networking technologies such as Software Defined Networking and Network Function Virtualization provide effective means for implementing these dynamic defense methods. To encourage researchers and industry experts in using such defenses, we highlight industry use-cases and discuss the practicality and maturity of these defenses. To aid readers who want to test or deploy MTD techniques, we highlight existing MTD test-beds. Our survey then performs both a qualitative and quantitative analysis to better understand the effectiveness of these MTDs in terms of security and performance. To that extent, we use well-defined metrics for measuring performance costs and security impacts of the surveyed MTDs. Finally, we conclude by summarizing research opportunities that our survey elucidates.

Connected through hyperedge HE-1 (induced by source paper "spatial temporal moving target defense a markov stackelberg game model"):
  with members:
    - "optimal timing of moving target defense a stackelberg game model"
    - "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"

Connected through hyperedge HE-2 (induced by source paper "a receding horizon mdp approach for performance evaluation of moving target defense in networks"):
  with members:
    - "exact inference techniques for the analysis of bayesian attack graphs"
    - "technical aspects of cyber kill chain"

Connected through hyperedge HE-3 (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"
    - "frvm flexible random virtual ip multiplexing in software defined networks"
    - "cost benefit analysis of moving target defense in power grids"
    - "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)