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 on the trade off between utility and information leakage"
  Abstract: Differential privacy is a notion of privacy that has become very popular in the database community. Roughly, the idea is that a randomized query mechanism provides sufficient privacy protection if the ratio between the probabilities that two adjacent datasets give the same answer is bound by ee. In the field of information flow there is a similar concern for controlling information leakage, i.e. limiting the possibility of inferring the secret information from the observables. In recent years, researchers have proposed to quantify the leakage in terms of min-entropy leakage, a concept strictly related to the Bayes risk. In this paper, we show how to model the query system in terms of an information-theoretic channel, and we compare the notion of differential privacy with that of min-entropy leakage. We show that differential privacy implies a bound on the min-entropy leakage, but not vice-versa. Furthermore, we show that our bound is tight. Then, we consider the utility of the randomization mechanism, which represents how close the randomized answers are to the real ones, in average. We show that the notion of differential privacy implies a bound on utility, also tight, and we propose a method that under certain conditions builds an optimal randomization mechanism, i.e. a mechanism which provides the best utility while guaranteeing e-differential privacy.

Connected through hyperedge HE-1 (induced by source paper "error bounds and guidelines for privacy calibration in differentially private kalman filtering"):
  with members:
    - "differentially private mimo filtering for event streams and spatio temporal monitoring"
    - "differential privacy an economic method for choosing epsilon"

Connected through hyperedge HE-2 (induced by source paper "understanding membership inferences on well generalized learning models"):
  with members:
    - "robust physical world attacks on deep learning models"
    - "towards measuring membership privacy"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "machine learning models that remember too much"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "stealing machine learning models via prediction apis"
    - "delving into transferable adversarial examples and black box attacks"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-3 (induced by source paper "minimax filter learning to preserve privacy from inference attacks"):
  with members:
    - "learning privately from multiparty data"
    - "calibrating data to sensitivity in private data analysis"
    - "privacy against statistical inference"
    - "discriminately decreasing discriminability with learned image filters"
    - "an information theoretic approach to privacy"
    - "differential privacy via wavelet transforms"
    - "what can we learn privately"

Connected through hyperedge HE-4 (induced by source paper "differential privacy as a mutual information constraint"):
  with members:
    - "on the continuity of the secrecy capacity of compound and arbitrarily varying wiretap channels"
    - "resolvability in e γ with applications to lossy compression and wiretap channels"
    - "on the relation between identifiability differential privacy and mutual information privacy"
    - "the composition theorem for differential privacy"
    - "lower bounds in differential privacy"

Connected through hyperedge HE-5 (induced by source paper "privacy games optimal user centric data obfuscation"):
  with members:
    - "privacy tradeoffs in predictive analytics"
    - "optimal geo indistinguishable mechanisms for location privacy"
    - "blowfish privacy tuning privacy utility trade offs using policies"
    - "prolonging the hide and seek game optimal trajectory privacy for location based services"
    - "a predictive differentially private mechanism for mobility traces"
    - "geo indistinguishability differential privacy for location based systems"
    - "calibrating data to sensitivity in private data analysis"
    - "the optimal mechanism in differential privacy"

Connected through hyperedge HE-6 (induced by source paper "on the relation between identifiability differential privacy and mutual information privacy"):
  with members:
    - "a minimax distortion view of differentially private query release"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "fingerprinting codes and the price of approximate differential privacy"
    - "optimal private halfspace counting via discrepancy"
    - "calibrating data to sensitivity in private data analysis"
    - "privacy against statistical inference"
    - "a learning theory approach to non interactive database privacy"
    - "lower bounds in differential privacy"

Connected through hyperedge HE-7 (induced by source paper "utilizing noise addition for data privacy an overview"):
  with members:
    - "utility and privacy of data sources can shannon help conceal and reveal information"
    - "a noise addition scheme in decision tree for privacy preserving data mining"
    - "composition attacks and auxiliary information in data privacy"

Connected through hyperedge HE-8 (induced by source paper "on the relation between differential privacy and quantitative information flow"):
  with members:
    - "compositional closure for bayes risk in probabilistic noninterference"
    - "differential privacy versus quantitative information flow"
    - "universally utility maximizing privacy mechanisms"

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)