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: "estimation efficiency under privacy constraints"
  Abstract: We investigate the problem of estimating a random variable    $Y$    under a privacy constraint dictated by another correlated random variable    $X$   . When    $X$    and    $Y$    are discrete, we express the underlying privacy-utility tradeoff in terms of the privacy-constrained guessing probability    ${\mathcal {h}}(P_{XY}, \varepsilon)$   , and the maximum probability    $\mathsf {P}_{\mathsf {c}}(Y|Z)$    of correctly guessing    $Y$    given an auxiliary random variable    $Z$   , where the maximization is taken over all    $P_{Z|Y}$    ensuring that    $\mathsf {P}_{\mathsf {c}}(X|Z)\leq \varepsilon $    for a given privacy threshold    $\varepsilon \geq 0$   . We prove that    ${\mathcal {h}}(P_{XY}, \cdot)$    is concave and piecewise linear, which allows us to derive its expression in closed form for any    $\varepsilon $    when    $X$    and    $Y$    are binary. In the non-binary case, we derive    ${\mathcal {h}}(P_{XY}, \varepsilon)$    in the high-utility regime (i.e., for sufficiently large, but nontrivial, values of    $\varepsilon $   ) under the assumption that    $Y$    and    $Z$    have the same alphabets. We also analyze the privacy-constrained guessing probability for two scenarios in which    $X$   ,    $Y$   , and    $Z$    are binary vectors. When    $X$    and    $Y$    are continuous random variables, we formulate the corresponding privacy-utility tradeoff in terms of    ${\mathsf {sENSR}}(P_{XY}, \varepsilon)$   , the smallest normalized minimum mean squared-error (mmse) incurred in estimating    $Y$    from a Gaussian perturbation    $Z$   . Here, the minimization is taken over a family of Gaussian perturbations    $Z$    for which the mmse of    $f(X)$    given    $Z$    is within a factor    $1- \varepsilon $    from the variance of    $f(X)$    for any non-constant real-valued function    $f$   . We derive tight upper and lower bounds for    ${\mathsf {sENSR}}$    when    $Y$    is Gaussian. For general absolutely continuous random variables, we obtain a tight lower bound for    ${\mathsf {sENSR}}(P_{XY}, \varepsilon)$    in the high privacy regime, i.e., for small    $\varepsilon $   .

Connected through hyperedge HE-1 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "machine learning using the variational predictive information bottleneck with a validation set"
    - "on the difference between the information bottleneck and the deep information bottleneck"
    - "extracting robust and accurate features via a robust information bottleneck"
    - "general information bottleneck objectives and their applications to machine learning"
    - "vector gaussian ceo problem under logarithmic loss"
    - "on universal features for high dimensional learning and inference"
    - "variational predictive information bottleneck"

Connected through hyperedge HE-2 (induced by source paper "obfuscation via information density estimation"):
  with members:
    - "privacy amplification by mixing and diffusion mechanisms"
    - "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"
    - "an operational approach to information leakage"
    - "a tunable measure for information leakage"
    - "generating natural language adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "privacy against brute force inference attacks"):
  with members:
    - "a tunable measure for information leakage"
    - "privacy utility tradeoffs under constrained data release mechanisms"
    - "multi user guesswork and brute force security"
    - "from the information bottleneck to the privacy funnel"
    - "privacy against statistical inference"
    - "guessing revisited a large deviations approach"

Connected through hyperedge HE-4 (induced by source paper "on the robustness of information theoretic privacy measures and mechanisms"):
  with members:
    - "data disclosure under perfect sample privacy"
    - "privacy against brute force inference attacks"
    - "data privacy for a rho recoverable function"
    - "an operational approach to information leakage"
    - "the utility cost of robust privacy guarantees"
    - "privacy under hard distortion constraints"
    - "a tunable measure for information leakage"
    - "technical privacy metrics a systematic survey"

Connected through hyperedge HE-5 (induced by source paper "an operational approach to information leakage"):
  with members:
    - "technical privacy metrics a systematic survey"
    - "measuring secrecy by the probability of a successful guess"
    - "principal inertia components and applications"
    - "privacy utility tradeoffs under constrained data release mechanisms"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "a large deviations approach to secure lossy compression"
    - "mitigating timing side channel in shared schedulers"
    - "hiding the rumor source"

Connected through hyperedge HE-6 (induced by source paper "optimal utility privacy trade off with the total variation distance as the privacy measure"):
  with members:
    - "the utility cost of robust privacy guarantees"
    - "a tunable measure for information leakage"
    - "vgan based image representation learning for privacy preserving facial expression recognition"
    - "privacy preserving adversarial networks"
    - "privacy aware guessing efficiency"
    - "privacy utility tradeoffs under constrained data release mechanisms"
    - "an estimation theoretic view of privacy"
    - "privacy aware mmse estimation"

Connected through hyperedge HE-7 (induced by source paper "data privacy for a rho recoverable function"):
  with members:
    - "context aware generative adversarial privacy"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"
    - "local private efficient protocols for succinct histograms"
    - "renyi divergence and kullback leibler divergence"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "the composition theorem for differential privacy"
    - "privacy against statistical inference"
    - "a learning theory approach to non interactive database privacy"

Connected through hyperedge HE-8 (induced by source paper "the utility cost of robust privacy guarantees"):
  with members:
    - "an estimation theoretic view of privacy"
    - "f divergence inequalities"
    - "strong data processing inequalities for input constrained additive noise channels"
    - "information extraction under privacy constraints"
    - "strong data processing inequalities and phi sobolev inequalities for discrete channels"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "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)