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: "asymptotic loss in privacy due to dependency in gaussian traces"
  Abstract: Rapid growth of the Internet of Things (IoT) necessitates employing privacy-preserving techniques to protect users' sensitive information. Even when user traces are anonymized, statistical matching can be employed to infer sensitive information. In our previous work, we have established the privacy requirements for the case that the user traces are instantiations of discrete random variables and the adversary knows only the structure of the dependency graph, i.e., whether each pair of users is connected. In this paper, we consider the case where data traces are instantiations of Gaussian random variables and the adversary knows not only the structure of the graph but also the pairwise correlation coefficients. We establish the requirements on anonymization to thwart such statistical matching, which demonstrate the (significant) degree to which knowledge of the pairwise correlation coefficients further significantly aids the adversary in breaking user anonymity.

Connected through hyperedge HE-1 (induced by source paper "asymptotic limits of privacy in bayesian time series matching"):
  with members:
    - "privacy of dependent users against statistical matching"
    - "fundamental limits of invisible flow fingerprinting"
    - "privacy against statistical matching inter user correlation"
    - "towards provably invisible network flow fingerprints"
    - "matching anonymized and obfuscated time series to users profiles"
    - "where you are is who you are user identification by matching statistics"
    - "achieving perfect location privacy in wireless devices using anonymization"
    - "asymptotically optimal matching of multiple sequences to source distributions and training sequences"

Connected through hyperedge HE-2 (induced by source paper "privacy of dependent users against statistical matching"):
  with members:
    - "asymptotic limits of privacy in bayesian time series matching"
    - "a concentration of measure approach to database de anonymization"
    - "single server single message online private information retrieval with side information"
    - "fundamental limits of database alignment"
    - "community detection with side information exact recovery under the stochastic block model"
    - "matching graphs with community structure a concentration of measure approach"
    - "on the performance of a canonical labeling for matching correlated erdős renyi graphs"
    - "typicality matching for pairs of correlated graphs"

Connected through hyperedge HE-3 (induced by source paper "fundamental limits of covert bit insertion in packets"):
  with members:
    - "first and second order asymptotics in covert communication"
    - "embedding covert information in broadcast communications"
    - "fundamental limits of invisible flow fingerprinting"
    - "privacy preserving mhealth data release with pattern consistency"
    - "privacy against statistical matching inter user correlation"
    - "towards provably invisible network flow fingerprints"
    - "matching anonymized and obfuscated time series to users profiles"
    - "covert wireless communication with artificial noise generation"

Connected through hyperedge HE-4 (induced by source paper "fundamental limits of invisible flow fingerprinting"):
  with members:
    - "fundamental limits of covert bit insertion in packets"
    - "privacy preserving mhealth data release with pattern consistency"
    - "privacy against statistical matching inter user correlation"
    - "towards provably invisible network flow fingerprints"
    - "matching anonymized and obfuscated time series to users profiles"
    - "covert wireless communication with artificial noise generation"
    - "covert communications on renewal packet channels"
    - "covert communications on poisson packet channels"

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.IT(Information Theory)