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 against statistical matching inter user correlation"):
  Abstract: Modern applications significantly enhance user experience by adapting to each user's individual condition and/or preferences. While this adaptation can greatly improve utility or be essential for the application to work (e.g., for ride-sharing applications), the exposure of user data to the application presents a significant privacy threat to the users, 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 when traces are independent between users, and that independent obfuscation of the data traces is often insufficient to remedy such. Finally, we discuss how the users can employ dependency in their obfuscation to improve their privacy.

Includes members:
  Member "a smart home is no castle privacy vulnerabilities of encrypted iot traffic":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "iot behavioral monitoring via network traffic analysis")
  Member "matching anonymized and obfuscated time series to users profiles":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "privacy aware time series data sharing with deep reinforcement learning")
  Member "where you are is who you are user identification by matching statistics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "privacy in trajectory micro data publishing a survey")
  Member "achieving perfect location privacy in wireless devices using anonymization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "privacy aware time series data sharing with deep reinforcement learning")
  Member "asymptotically optimal matching of multiple sequences to source distributions and training sequences":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal resolution of change point detection with empirically observed statistics and erasures")
  Member "protecting locations with differential privacy under temporal correlations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "protect edge privacy in path publishing with differential privacy")

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)