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: "individual differential privacy a utility preserving formulation of differential privacy guarantees"
  Abstract: Differential privacy is a popular privacy model within the research community because of the strong privacy guarantee it offers, namely that the presence or absence of any individual in a data set does not significantly influence the results of analyses on the data set. However, enforcing this strict guarantee in practice significantly distorts data and/or limits data uses, thus diminishing the analytical utility of the differentially private results. In an attempt to address this shortcoming, several relaxations of differential privacy have been proposed that trade off privacy guarantees for improved data utility. In this paper, we argue that the standard formalization of differential privacy is stricter than required by the intuitive privacy guarantee it seeks. In particular, the standard formalization requires indistinguishability of results between  any  pair of neighbor data sets, while indistinguishability between  the actual  data set and its neighbor data sets should be enough. This limits the data controller’s ability to adjust the level of protection to the actual data, hence resulting in significant accuracy loss. In this respect, we propose  individual differential privacy , an alternative differential privacy notion that offers  the same privacy guarantees as standard differential privacy to individuals  (even though not to groups of individuals). This new notion allows the data controller to adjust the distortion to the actual data set, which results in less distortion and more analytical accuracy. We propose several mechanisms to attain  individual differential privacy  and we compare the new notion against standard differential privacy in terms of the accuracy of the analytical results.

Connected through hyperedge HE-1 (induced by source paper "mitigating query flooding parameter duplication attack on regression models with high dimensional gaussian mechanism"):
  with members:
    - "mvg mechanism differential privacy under matrix valued query"
    - "stealing neural networks via timing side channels"
    - "prada protecting against dnn model stealing attacks"
    - "utility optimized local differential privacy mechanisms for distribution estimation"
    - "stealing hyperparameters in machine learning"
    - "model extraction warning in mlaas paradigm"
    - "practical black box attacks against machine learning"
    - "stealing machine learning models via prediction apis"

Connected through hyperedge HE-2 (induced by source paper "privgen preserving privacy of sequences through data generation"):
  with members:
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "privacy preserving prediction"
    - "understanding membership inferences on well generalized learning models"
    - "plausible deniability for privacy preserving data synthesis"
    - "k τ e anonymity towards privacy preserving publishing of spatiotemporal trajectory data"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "privtree a differentially private algorithm for hierarchical decompositions"

Connected through hyperedge HE-3 (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-4 (induced by source paper "efficient data perturbation for privacy preserving and accurate data stream mining"):
  with members:
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "benchmarking distributed stream processing platforms for iot applications"
    - "extremal mechanisms for local differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "composition attacks and auxiliary information in data privacy"

Connected through hyperedge HE-5 (induced by source paper "optimization of privacy utility trade offs under informational self determination"):
  with members:
    - "privacy enhancing aggregation of internet of things data via sensors grouping"
    - "a theory of pricing private data"
    - "the composition theorem for 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)