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: "truncated laplacian mechanism for approximate differential privacy"
  Abstract: We derive a class of noise probability distributions to preserve $(\epsilon, \delta)$-differential privacy for single real-valued query function. The proposed noise distribution has a truncated exponential probability density function, which can be viewed as a truncated Laplacian distribution. We show the near-optimality of the proposed \emph{truncated Laplacian} mechanism in various privacy regimes in the context of minimizing the noise amplitude and noise power. Numeric experiments show the improvement of the truncated Laplacian mechanism over the optimal Gaussian mechanism by significantly reducing the noise amplitude and noise power in various privacy regions.

Connected through hyperedge HE-1 (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"
    - "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"
    - "renyi differential privacy"

Connected through hyperedge HE-2 (induced by source paper "the podium mechanism improving on the laplace and staircase mechanisms"):
  with members:
    - "optimality of the laplace mechanism in differential privacy"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "extremal mechanisms for local differential privacy"
    - "distributed private heavy hitters"
    - "calibrating data to sensitivity in private data analysis"
    - "functional mechanism regression analysis under differential privacy"
    - "private data release via learning thresholds"
    - "a simple and practical algorithm for differentially private data release"

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)