human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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 $\alpha=\infty$.

Connected through hyperedge HE-1 (induced by source paper "r enyi fair inference"):
  with members:
    - "fair logistic regression an adversarial perspective"
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "scalable fair clustering"
    - "unleashing linear optimizers for group fair learning and optimization"
    - "on the cost of essentially fair clusterings"
    - "privacy preserving clustering with constraints"
    - "fair clustering through fairlets"
    - "on the convergence and robustness of training gans with regularized optimal transport"

Connected through hyperedge HE-2 (induced by source paper "developing non stochastic privacy preserving policies using agglomerative clustering"):
  with members:
    - "robustness of maximal alpha leakage to side information"
    - "a submodularity based agglomerative clustering algorithm for the privacy funnel"
    - "privacy preserving prediction"
    - "uncertain wiretap channels and secure estimation"
    - "notes on information theoretic privacy"
    - "from the information bottleneck to the privacy funnel"
    - "a nonstochastic information theory for communication and state estimation"
    - "privacy against statistical inference"

Question: Please tell me which class the center node 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)