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: "linkmirage how to anonymize links in dynamic social systems"
  Abstract: Social network based trust relationships present a critical foundation for designing trustworthy systems, such as Sybil defenses, secure routing, and anonymous/censorshipresilient communications. A key issue in the design of such systems, is the revelation of users' trusted social contacts to an adversary-information that is considered sensitive in today's society. #R##N#In this work, we focus on the challenge of preserving the privacy of users' social contacts, while still enabling the design of social trust based applications. First, we propose LinkMirage, a community detection based algorithm for anonymizing links in social network topologies; LinkMirage preserves community structures in the social topology while anonymizing links within the communities. LinkMirage considers the evolution of the social network topologies, and minimizes privacy leakage due to temporal dynamics of the system. #R##N#Second, we define metrics for quantifying the privacy and utility of a time series of social topologies with anonymized links. We analyze the privacy and utility provided by LinkMirage both theoretically, as well as using real world social network topologies: a Facebook dataset with 870K links and a large-scale Google+ dataset with 940M links. We find that our approach significantly outperforms the existing state-of-art. #R##N#Finally, we demonstrate the applicability of LinkMirage in real-world applications such as Sybil defenses, reputation systems, anonymity systems and vertex anonymity. We also prototype LinkMirage as a Facebook application such that real world systems can bootstrap privacy-preserving trust relationships without the cooperation of the OSN operators.

Connected through hyperedge HE-1 (induced by source paper "privacy preserving social media data publishing"):
  with members:
    - "your actions tell where you are uncovering twitter users in a metropolitan area"
    - "geo indistinguishability differential privacy for location based systems"
    - "calibrating data to sensitivity in private data analysis"
    - "preserving link privacy in social network based systems"
    - "a learning theory approach to non interactive database privacy"
    - "compressive mechanism utilizing sparse representation in differential privacy"
    - "differential privacy via wavelet transforms"
    - "de anonymizing social networks"

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.SI(Social and Information Networks)