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: "the optimal mechanism in differential privacy"
  Abstract: We derive the optimal $\epsilon$-differentially private mechanism for single real-valued query function under a very general utility-maximization (or cost-minimization) framework. The class of noise probability distributions in the optimal mechanism has {\em staircase-shaped} probability density functions which are symmetric (around the origin), monotonically decreasing and geometrically decaying. The staircase mechanism can be viewed as a {\em geometric mixture of uniform probability distributions}, providing a simple algorithmic description for the mechanism. Furthermore, the staircase mechanism naturally generalizes to discrete query output settings as well as more abstract settings. We explicitly derive the optimal noise probability distributions with minimum expectation of noise amplitude and power. Comparing the optimal performances with those of the Laplacian mechanism, we show that in the high privacy regime ($\epsilon$ is small), Laplacian mechanism is asymptotically optimal as $\epsilon \to 0$; in the low privacy regime ($\epsilon$ is large), the minimum expectation of noise amplitude and minimum noise power are $\Theta(\Delta e^{-\frac{\epsilon}{2}})$ and $\Theta(\Delta^2 e^{-\frac{2\epsilon}{3}})$ as $\epsilon \to +\infty$, while the expectation of noise amplitude and power using the Laplacian mechanism are $\frac{\Delta}{\epsilon}$ and $\frac{2\Delta^2}{\epsilon^2}$, where $\Delta$ is the sensitivity of the query function. We conclude that the gains are more pronounced in the low privacy regime.

Connected through hyperedge HE-1 (induced by source paper "free gap information from the differentially private sparse vector and noisy max mechanisms"):
  with members:
    - "proving differential privacy with shadow execution"
    - "private selection from private candidates"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "ektelo a framework for defining differentially private computations"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "synthesizing coupling proofs of differential privacy"
    - "renyi differential privacy"
    - "towards practical differential privacy for sql queries"

Connected through hyperedge HE-2 (induced by source paper "reviewing and improving the gaussian mechanism for differential privacy"):
  with members:
    - "privacy and utility tradeoff in approximate differential privacy"
    - "the podium mechanism improving on the laplace and staircase mechanisms"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "truncated laplacian mechanism for approximate differential privacy"
    - "private model compression via knowledge distillation"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "knock knock who s there membership inference on aggregate location data"
    - "local differential privacy for physical sensor data and sparse recovery"

Connected through hyperedge HE-3 (induced by source paper "differentially private sql with bounded user contribution"):
  with members:
    - "sok differential privacies"
    - "detecting violations of differential privacy"
    - "shrinkwrap differentially private query processing in private data federations"
    - "plausible deniability for privacy preserving data synthesis"
    - "renyi differential privacy"
    - "towards practical differential privacy for sql queries"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "differential privacy an estimation theory based method for choosing epsilon"

Connected through hyperedge HE-4 (induced by source paper "development and analysis of deterministic privacy preserving policies using non stochastic information theory"):
  with members:
    - "uncertain wiretap channels and secure estimation"
    - "extremal mechanisms for local differential privacy"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "a nonstochastic information theory for communication and state estimation"
    - "information masking and amplification the source coding setting"
    - "privacy against statistical inference"
    - "on the relation between differential privacy and quantitative information flow"

Connected through hyperedge HE-5 (induced by source paper "information theoretic privacy through chaos synchronization and optimal additive noise"):
  with members:
    - "on privacy of quantized sensor measurements through additive noise"
    - "managing your private and public data bringing down inference attacks against your privacy"
    - "privacy constrained information processing"
    - "increasing smart meter privacy through energy harvesting and storage devices"
    - "privacy against statistical inference"
    - "smart meter privacy a utility privacy framework"

Connected through hyperedge HE-6 (induced by source paper "differential privacy techniques for cyber physical systems a survey"):
  with members:
    - "quantifying differential privacy in continuous data release under temporal correlations"
    - "sok differential privacies"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "achieving differential privacy against non intrusive load monitoring in smart grid a fog computing approach"
    - "toward practical privacy preserving analytics for iot and cloud based healthcare systems"
    - "privacy preserving distributed deep learning for clinical data"
    - "re dpoctor real time health data releasing with w day differential privacy"

Connected through hyperedge HE-7 (induced by source paper "on privacy of quantized sensor measurements through additive noise"):
  with members:
    - "on reachable sets of hidden cps sensor attacks"
    - "constraining attacker capabilities through actuator saturation"
    - "a comparison of stealthy sensor attacks on control systems"
    - "managing your private and public data bringing down inference attacks against your privacy"
    - "increasing smart meter privacy through energy harvesting and storage devices"
    - "privacy against statistical inference"

Connected through hyperedge HE-8 (induced by source paper "optimal differentially private mechanisms for randomised response"):
  with members:
    - "differentially private response mechanisms on categorical data"
    - "extremal mechanisms for local differential privacy"

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.CR(Cryptography and Security)