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: "an operational approach to information leakage"
  Abstract: Given two random variables $X$ and $Y$, an operational approach is undertaken to quantify the ``leakage'' of information from $X$ to $Y$. The resulting measure $\mathcal{L}(X \!\! \to \!\! Y)$ is called \emph{maximal leakage}, and is defined as the multiplicative increase, upon observing $Y$, of the probability of correctly guessing a randomized function of $X$, maximized over all such randomized functions. A closed-form expression for $\mathcal{L}(X \!\! \to \!\! Y)$ is given for discrete $X$ and $Y$, and it is subsequently generalized to handle a large class of random variables. The resulting properties are shown to be consistent with an axiomatic view of a leakage measure, and the definition is shown to be robust to variations in the setup. Moreover, a variant of the Shannon cipher system is studied, in which performance of an encryption scheme is measured using maximal leakage. A single-letter characterization of the optimal limit of (normalized) maximal leakage is derived and asymptotically-optimal encryption schemes are demonstrated. Furthermore, the sample complexity of estimating maximal leakage from data is characterized up to subpolynomial factors. Finally, the \emph{guessing} framework used to define maximal leakage is used to give operational interpretations of commonly used leakage measures, such as Shannon capacity, maximal correlation, and local differential privacy.

Connected through hyperedge HE-1 (induced by source paper "the capacity of single server weakly private information retrieval"):
  with members:
    - "x secure t private information retrieval from mds coded storage with byzantine and unresponsive servers"
    - "on the information leakage in private information retrieval systems"
    - "weakly private information retrieval"
    - "on the capacity of private information retrieval from coded colluding and adversarial servers"
    - "asymmetry helps improved private information retrieval protocols for distributed storage"
    - "lifting private information retrieval from two to any number of messages"
    - "capacity achieving private information retrieval codes with optimal message size and upload cost"
    - "on the access complexity of pir schemes"

Connected through hyperedge HE-2 (induced by source paper "robust generalization via α mutual information"):
  with members:
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "learners that use little information"
    - "generalization in adaptive data analysis and holdout reuse"
    - "renyi divergence and kullback leibler divergence"

Connected through hyperedge HE-3 (induced by source paper "privacy utility tradeoff in a guessing framework inspired by index coding"):
  with members:
    - "from the information bottleneck to the privacy funnel"
    - "lexicographic products and the power of non linear network coding"
    - "broadcasting with side information"

Connected through hyperedge HE-4 (induced by source paper "obfuscation via information density estimation"):
  with members:
    - "privacy amplification by mixing and diffusion mechanisms"
    - "estimation efficiency under privacy constraints"
    - "distributed generation of privacy preserving data with user customization"
    - "on the robustness of information theoretic privacy measures and mechanisms"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "generative adversarial privacy"
    - "a tunable measure for information leakage"
    - "generating natural language adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "a necessary and sufficient stability notion for adaptive generalization"):
  with members:
    - "natural analysts in adaptive data analysis"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "calibrating noise to variance in adaptive data analysis"
    - "learners that use little information"
    - "learners that leak little information"
    - "typicality based stability and privacy"
    - "inferential privacy guarantees for differentially private mechanisms"
    - "adaptive learning with robust generalization guarantees"

Connected through hyperedge HE-6 (induced by source paper "generalization error bounds via renyi f divergences and maximal leakage"):
  with members:
    - "tightening mutual information based bounds on generalization error"
    - "generalization error bounds for noisy iterative algorithms"
    - "chaining mutual information and tightening generalization bounds"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "learners that use little information"
    - "dependence measures bounding the exploration bias for general measurements"
    - "max information differential privacy and post selection hypothesis testing"
    - "generalization in adaptive data analysis and holdout reuse"

Connected through hyperedge HE-7 (induced by source paper "noiseless privacy"):
  with members:
    - "uncertain wiretap channels and secure estimation"
    - "on the relation between identifiability differential privacy and mutual information privacy"
    - "extremal mechanisms for local differential privacy"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "a nonstochastic information theory for communication and state estimation"
    - "the composition theorem for differential privacy"
    - "information masking and amplification the source coding setting"
    - "privacy against statistical inference"

Connected through hyperedge HE-8 (induced by source paper "robustness of maximal alpha leakage to side information"):
  with members:
    - "a tunable measure for information leakage"
    - "privacy aware guessing efficiency"
    - "renyi differential privacy"
    - "on the semantics of differential privacy a bayesian formulation"

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)