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: "privacy of dependent users against statistical matching"
  Abstract: Modern applications significantly enhance user experience by adapting to each user's individual condition and/or preferences. While this adaptation can greatly improve a user's experience or be essential for the application to work, the exposure of user data to the application presents a significant privacy threat to the users\textemdash even when the traces are anonymized (since the statistical matching of an anonymized trace to prior user behavior can identify a user and their habits). Because of the current and growing algorithmic and computational capabilities of adversaries, provable privacy guarantees as a function of the degree of anonymization and obfuscation of the traces are necessary. Our previous work has established the requirements on anonymization and obfuscation in the case that data traces are independent between users. However, the data traces of different users will be dependent in many applications, and an adversary can potentially exploit such. In this paper, we consider the impact of correlation between user traces on their privacy. First, we demonstrate that the adversary can readily identify the association graph, revealing which user data traces are correlated. Next, we demonstrate that the adversary can use this association graph to break user privacy with significantly shorter traces than in the case of independent users, and that obfuscating data traces independently across users is often insufficient to remedy such leakage. Finally, we discuss how users can employ data trace dependency to improve privacy by performing better obfuscation.

Connected through hyperedge HE-1 (induced by source paper "asymptotic limits of privacy in bayesian time series matching"):
  with members:
    - "asymptotic loss in privacy due to dependency in gaussian traces"
    - "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 "leveraging prior knowledge asymmetries in the design of location privacy preserving mechanisms"):
  with members:
    - "matching anonymized and obfuscated time series to users profiles"
    - "is geo indistinguishability what you are looking for"
    - "a survey of point of interest recommendation in location based social networks"
    - "optimal geo indistinguishable mechanisms for location privacy"

Connected through hyperedge HE-3 (induced by source paper "asymptotic loss in privacy due to dependency in gaussian traces"):
  with members:
    - "privacy against statistical matching inter user correlation"
    - "towards provably invisible network flow fingerprints"
    - "matching anonymized and obfuscated time series to users profiles"

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)