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 "the privacy funnel from the viewpoint of local differential privacy"):
  Abstract: We consider a database $\vec{X} = (X_1,\cdots,X_n)$ containing the data of $n$ users. The data aggregator wants to publicise the database, but wishes to sanitise the dataset to hide sensitive data $S_i$ correlated to $X_i$. This setting is considered in the Privacy Funnel, which uses mutual information as a leakage metric. The downsides to this approach are that mutual information does not give worst-case guarantees, and that finding optimal sanitisation protocols can be computationally prohibitive. We tackle these problems by using differential privacy metrics, and by considering local protocols which operate on one entry at a time. We show that under both the Local Differential Privacy and Local Information Privacy leakage metrics, one can efficiently obtain optimal protocols; however, Local Information Privacy is both more closely aligned to the privacy requirements of the Privacy Funnel scenario, and more efficiently computable. We also consider the scenario where each user has multiple attributes (i.e. $X_i = (X^1_i,\cdots,X^m_i)$), for which we define \emph{Side-channel Resistant Local Information Privacy}, and we give efficient methods to find protocols satisfying this criterion while still offering good utility. Exploratory experiments confirm the validity of these methods.

Includes members:
  Member "encode shuffle analyze privacy revisited formalizations and empirical evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "privacy preserving bandits")
  Member "a submodularity based agglomerative clustering algorithm for the privacy funnel":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "developing non stochastic privacy preserving policies using agglomerative clustering")
  Member "information theoretic metrics for local differential privacy protocols":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "four accuracy bounds and one estimator for frequency estimation under local differential privacy")
  Member "on perfect privacy and maximal correlation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "data disclosure under perfect sample privacy")
  Member "principal inertia components and applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "task based quantization with application to mimo receivers")
  Member "differential privacy as a mutual information constraint":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "local information privacy and its application to privacy preserving data aggregation")
  Member "extremal mechanisms for local differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a workload adaptive mechanism for linear queries under local differential privacy")
  Member "what can we learn privately":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "locally private hypothesis selection")

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)