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 "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.

Includes members:
  Member "toward a theory of cyber attacks":
    also connected through other hyperedges:
      (none)
  Member "adversarial deep learning for robust detection of binary encoded malware":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "effectiveness of adversarial examples and defenses for malware classification")
  Member "adaptive mtd security using markov game modeling":
    also connected through other hyperedges:
      (none)
  Member "trufl distributed trust management framework in sdn":
    also connected through other hyperedges:
      (none)
  Member "markov game modeling of moving target defense for strategic detection of threats in cloud networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "toward proactive adaptive defense a survey on moving target defense")
  Member "mtdeep boosting the security of deep neural nets against adversarial attacks with moving target defense":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "advms a multi source multi cost defense against adversarial attacks")
  Member "moving target defense for web applications using bayesian stackelberg games":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "flipin a game theoretic cyber insurance framework for incentive compatible cyber risk management of internet of things")
  Member "software defined networking a comprehensive survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")

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.CR(Cryptography and Security)