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 "conditional analysis for key value data with local differential privacy"):
  Abstract: Local differential privacy (LDP) has been deemed as the de facto measure for privacy-preserving distributed data collection and analysis. Recently, researchers have extended LDP to the basic data type in NoSQL systems: the key-value data, and show its feasibilities in mean estimation and frequency estimation. In this paper, we develop a set of new perturbation mechanisms for key-value data collection and analysis under the strong model of local differential privacy. Since many modern machine learning tasks rely on the availability of conditional probability or the marginal statistics, we then propose the conditional frequency estimation method for key analysis and the conditional mean estimation for value analysis in key-value data. The released statistics with conditions can further be used in learning tasks. Extensive experiments of frequency and mean estimation on both synthetic and real-world datasets validate the effectiveness and accuracy of the proposed key-value perturbation mechanisms against the state-of-art competitors.

Includes members:
  Member "locally differentially private data collection and analysis":
    also connected through other hyperedges:
      (none)
  Member "collecting and analyzing multidimensional data with local differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "locally private distributed reinforcement learning")
  Member "comparing population means under local differential privacy with significance and power":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "locally private distributed reinforcement learning")
  Member "practical locally private heavy hitters":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a workload adaptive mechanism for linear queries under local differential privacy")
  Member "marginal release under local differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "outis crypto assisted differential privacy on untrusted servers")
  Member "collecting telemetry data privately":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "locally private hypothesis selection")
  Member "collecting and analyzing data from smart device users with local differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "privacy preserving boosting in the local setting")
  Member "local private efficient protocols for succinct histograms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pure differentially private summation from anonymous messages")

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.CR(Cryptography and Security)