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 "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 $   .

Includes members:
  Member "technical privacy metrics a systematic survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ensuring privacy in location based services a model based approach")
  Member "generalizing bottleneck problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "obfuscation via information density estimation")
  Member "privacy aware guessing efficiency":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robustness of maximal alpha leakage to side information")
  Member "hypothesis testing under mutual information privacy constraints in the high privacy regime":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "privacy aware distributed hypothesis testing")
  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 "hypothesis testing under maximal leakage privacy constraints":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a practical approach to navigating the tradeoff between privacy and precise utility")
  Member "privacy aware mmse estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "local information privacy and its application to privacy preserving data aggregation")
  Member "notes on information theoretic privacy":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "data disclosure under perfect sample privacy")

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)