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 "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.

Includes members:
  Member "asymptotic limits of privacy in bayesian time series matching":
    also connected through other hyperedges:
      (none)
  Member "a concentration of measure approach to database de anonymization":
    also connected through other hyperedges:
      (none)
  Member "single server single message online private information retrieval with side information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "private computation with individual and joint privacy")
  Member "asymptotic loss in privacy due to dependency in gaussian traces":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "asymptotic limits of privacy in bayesian time series matching")
  Member "fundamental limits of database alignment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a concentration of measure approach to database de anonymization")
  Member "community detection with side information exact recovery under the stochastic block model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mutual information in community detection with covariate information and correlated networks")
  Member "matching graphs with community structure a concentration of measure approach":
    also connected through other hyperedges:
      (none)
  Member "on the performance of a canonical labeling for matching correlated erdős renyi graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fundamental limits of database alignment")

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.IT(Information Theory)