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: "differential privacy an estimation theory based method for choosing epsilon"
  Abstract: Differential privacy is achieved by the introduction of Laplacian noise in the response to a query, establishing a precise trade-off between the level of differential privacy and the accuracy of the database response (via the amount of noise introduced). However, the amount of noise to add is typically defined through the scale parameter of the Laplace distribution, whose use may not be so intuitive. In this paper we propose to use two parameters instead, related to the notion of interval estimation, which provide a more intuitive picture of how precisely the true output of a counting query may be gauged from the noise-polluted one (hence, how much the individual's privacy is protected).

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"
    - "individual differential privacy a utility preserving formulation of differential privacy guarantees"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-2 (induced by source paper "effects of differential privacy and data skewness on membership inference vulnerability"):
  with members:
    - "demystifying learning rate polices for high accuracy training of deep neural networks"
    - "cross layer strategic ensemble defense against adversarial examples"
    - "differentially private model publishing for deep learning"
    - "evaluating differentially private machine learning in practice"
    - "robust or private adversarial training makes models more vulnerable to privacy attacks"
    - "deep neural network ensembles against deception ensemble diversity accuracy and robustness"
    - "exploiting unintended feature leakage in collaborative learning"
    - "beyond inferring class representatives user level privacy leakage from federated learning"

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"
    - "a data and workload aware algorithm for range queries under differential privacy"

Connected through hyperedge HE-4 (induced by source paper "a conceptual framework for assessing anonymization utility trade offs based on principal component analysis"):
  with members:
    - "differential privacy for counting queries can bayes estimation help uncover the true value"

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)