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 "tunable measures for information leakage and applications to privacy utility tradeoffs"):
  Abstract: In the first half of the paper, we introduce a tunable measure for information leakage called \textit{maximal $\alpha$-leakage}. This measure quantifies the maximal gain of an adversary in refining a tilted version of its posterior belief of any (potentially random) function of a data set conditioning on a released data set. The choice of $\alpha$ determines the specific adversarial action ranging from refining a belief for $\alpha =1$ to guessing the best posterior for $\alpha = \infty$, and for these extremal values maximal $\alpha$-leakage simplifies to mutual information and maximal leakage, respectively. For $\alpha\in(1,\infty)$ this measure is shown to be the Arimoto channel capacity. We show that maximal $\alpha$-leakage satisfies data processing inequalities and sub-additivity (composition property). In the second half of the paper, we use maximal $\alpha$-leakage as the privacy measure and study the problem of data publishing with privacy guarantees, wherein the utility of the released data is ensured via a \emph{hard distortion} constraint. Unlike average distortion, hard distortion provides a deterministic guarantee of fidelity. We show that under a hard distortion constraint, both the optimal mechanism and the optimal tradeoff are invariant for any $\alpha>1$, and the tunable leakage measure only behaves as either of the two extrema, i.e., mutual information for $\alpha=1$ and maximal leakage for $\alpha=\infty$.

Includes members:
  Member "robustness of maximal alpha leakage to side information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "developing non stochastic privacy preserving policies using agglomerative clustering")
  Member "data privacy for a rho recoverable function":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the robustness of information theoretic privacy measures and mechanisms")
  Member "an operational approach to information leakage":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the capacity of single server weakly private information retrieval")
  Member "optimal utility privacy trade off with the total variation distance as the privacy measure":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mobile sensor data anonymization")
  Member "robust privacy utility tradeoffs under differential privacy and hamming distortion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "interactive secure function computation")
  Member "privacy aware guessing efficiency":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "estimation efficiency under privacy constraints")
  Member "renyi differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "guidelines for implementing and auditing differentially private systems")
  Member "hypothesis testing under mutual information privacy constraints in the high privacy regime":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "privacy aware distributed hypothesis testing")

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)