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 "matching anonymized and obfuscated time series to users profiles"):
  Abstract: Many popular applications use traces of user data to offer various services to their users. However, even if user data is anonymized and obfuscated, a user's privacy can be compromised through the use of statistical matching techniques that match a user trace to prior user behavior. In this work, we derive the theoretical bounds on the privacy of users in such a scenario. We build on our recent study in the area of location privacy, in which we introduced formal notions of location privacy for anonymization-based location privacy-protection mechanisms. Here we derive the fundamental limits of user privacy when both anonymization and obfuscation-based protection mechanisms are applied to users' time series of data. We investigate the impact of such mechanisms on the trade-off between privacy protection and user utility. We first study achievability results for the case where the time-series of users are governed by an i.i.d. process. The converse results are proved both for the i.i.d. case as well as the more general Markov chain model. We demonstrate that as the number of users in the network grows, the obfuscation-anonymization plane can be divided into two regions: in the first region, all users have perfect privacy; and, in the second region, no user has privacy.

Includes members:
  Member "robust privacy utility tradeoffs under differential privacy and hamming distortion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tunable measures for information leakage and applications to privacy utility tradeoffs")
  Member "towards provably invisible network flow fingerprints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fundamental limits of covert packet insertion")
  Member "a smart home is no castle privacy vulnerabilities of encrypted iot traffic":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "iot behavioral monitoring via network traffic analysis")
  Member "information theoretic privacy with general distortion constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "context aware generative adversarial privacy")
  Member "hypothesis testing under maximal leakage privacy constraints":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a practical approach to navigating the tradeoff between privacy and precise utility")
  Member "where you are is who you are user identification by matching statistics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "privacy aware time series data sharing with deep reinforcement learning")
  Member "hypothesis testing in the high privacy limit":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "privacy with estimation guarantees")

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)