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 local mean field analysis of security investments in networks"
  Abstract: Getting agents in the Internet, and in networks in general, to invest in and deploy security features and protocols is a challenge, in particular because of economic reasons arising from the presence of network externalities. Our goal in this paper is to carefully model and quantify the impact of such externalities on the investment in, and deployment of, security features and protocols in a network. #R##N#Specifically, we study a network of interconnected agents, which are subject to epidemic risks such as those caused by propagating viruses and worms, and which can decide whether or not to invest some amount to self-protect and deploy security solutions. We make three contributions in the paper. First, we introduce a general model which combines an epidemic propagation model with an economic model for agents which captures network effects and externalities. Second, borrowing ideas and techniques used in statistical physics, we introduce a Local Mean Field (LMF) model, which extends the standard mean-field approximation to take into account the correlation structure on local neighborhoods. Third, we solve the LMF model in a network with externalities, and we derive analytic solutions for sparse random graphs, for which we obtain asymptotic results. We explicitly identify the impact of network externalities on the decision to invest in and deploy security features. In other words, we identify both the economic and network properties that determine the adoption of security technologies.

Connected through hyperedge HE-1 (induced by source paper "cyber insurance"):
  with members:
    - "modeling and analysis of leaky deception using signaling games with evidence"
    - "a mean field stackelberg game approach for obfuscation adoption in empirical risk minimization"
    - "a dynamic game analysis and design of infrastructure network protection and recovery 125"
    - "a game theoretic taxonomy and survey of defensive deception for cybersecurity and privacy"
    - "a stackelberg game perspective on the conflict between machine learning and data obfuscation"
    - "deception by design evidence based signaling games for network defense"
    - "large scale complex it systems"

Connected through hyperedge HE-2 (induced by source paper "individual security and network design with malicious nodes"):
  with members:
    - "a secure approach for caching contents in wireless ad hoc networks"
    - "strategic network formation with attack and immunization"
    - "multidefender security games"
    - "interdependent defense games modeling interdependent security under deliberate attacks"

Connected through hyperedge HE-3 (induced by source paper "effects of degree correlations in interdependent security good or bad"):
  with members:
    - "internalization of externalities in interdependent security large network cases"
    - "hiding the rumor source"
    - "interdependent security with strategic agents and cascades of infection"

Connected through hyperedge HE-4 (induced by source paper "interdependent security with strategic agents and cascades of infection"):
  with members:
    - "community structure and scale free collections of erdős renyi graphs"

Connected through hyperedge HE-5 (induced by source paper "coordination in network security games a monotone comparative statics approach"):
  with members:
    - "selfish response to epidemic propagation"

Connected through hyperedge HE-6 (induced by source paper "the impact of secure oss on internet security what cyber insurers need to know"):
  with members:
    - "the evolution of cyberinsurance"

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.GT(Computer Science and Game Theory)