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: "on the relation between differential privacy and quantitative information flow"
  Abstract: Differential privacy is a notion that has emerged in the community of statistical databases, as a response to the problem of protecting the privacy of the database's participants when performing statistical queries. The idea is that a randomized query satisfies differential privacy if the likelihood of obtaining a certain answer for a database x is not too different from the likelihood of obtaining the same answer on adjacent databases, i.e. databases which differ from x for only one individual.#R##N##R##N#Information flow is an area of Security concerned with the problem of controlling the leakage of confidential information in programs and protocols. Nowadays, one of the most established approaches to quantify and to reason about leakage is based on the Renyi min entropy version of information theory.#R##N##R##N#In this paper, we analyze critically the notion of differential privacy in light of the conceptual framework provided by the Renyi min information theory. We show that there is a close relation between differential privacy and leakage, due to the graph symmetries induced by the adjacency relation. Furthermore, we consider the utility of the randomized answer, which measures its expected degree of accuracy. We focus on certain kinds of utility functions called "binary", which have a close correspondence with the Renyi min mutual information. Again, it turns out that there can be a tight correspondence between differential privacy and utility, depending on the symmetries induced by the adjacency relation and by the query. Depending on these symmetries we can also build an optimal-utility randomization mechanism while preserving the required level of differential privacy. Our main contribution is a study of the kind of structures that can be induced by the adjacency relation and the query, and how to use them to derive bounds on the leakage and achieve the optimal utility.

Connected through hyperedge HE-1 (induced by source paper "information theoretic metrics for local differential privacy protocols"):
  with members:
    - "consistent and accurate frequency oracles under local differential privacy"
    - "towards instance optimal private query release"
    - "distributed and secure ml with self tallying multi party aggregation"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "renyi differential privacy"
    - "achieving secure and differentially private computations in multiparty settings"
    - "collecting telemetry data privately"
    - "differential privacy as a mutual information constraint"

Connected through hyperedge HE-2 (induced by source paper "the complexity of verifying loop free programs as differentially private"):
  with members:
    - "automated methods for checking differential privacy"
    - "property testing for differential privacy"
    - "bisimilarity distances for approximate differential privacy"
    - "detecting violations of differential privacy"
    - "synthesizing coupling proofs of differential privacy"
    - "lightdp towards automating differential privacy proofs"
    - "understanding the sparse vector technique for differential privacy"
    - "proving differential privacy via probabilistic couplings"

Connected through hyperedge HE-3 (induced by source paper "noiseless privacy"):
  with members:
    - "an operational approach to information leakage"
    - "uncertain wiretap channels and secure estimation"
    - "on the relation between identifiability differential privacy and mutual information privacy"
    - "extremal mechanisms for local differential privacy"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "a nonstochastic information theory for communication and state estimation"
    - "the composition theorem for differential privacy"
    - "information masking and amplification the source coding setting"

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"
    - "the optimal mechanism in differential privacy"
    - "privacy against statistical inference"

Connected through hyperedge HE-5 (induced by source paper "the optimal differential privacy mechanism under hamming distortion for universal memoryless source classes"):
  with members:
    - "differential privacy as a mutual information constraint"
    - "information extraction under privacy constraints"
    - "on the relation between identifiability differential privacy and mutual information privacy"
    - "extremal mechanisms for local differential privacy"
    - "optimal noise adding mechanisms for approximate differential privacy"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "privacy against statistical inference"
    - "what can we learn privately"

Connected through hyperedge HE-6 (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-7 (induced by source paper "utility privacy tradeoffs in databases an information theoretic approach"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "privacy against statistical inference"
    - "on privacy preserving histograms"
    - "discriminatory lossy source coding side information privacy"
    - "competitive privacy in the smart grid an information theoretic approach"
    - "an information theoretic approach to privacy"
    - "secure source coding with a helper"
    - "privacy constraints in regularized convex optimization"

Connected through hyperedge HE-8 (induced by source paper "differential privacy on the trade off between utility and information leakage"):
  with members:
    - "differential privacy versus quantitative information flow"
    - "fuzzy extractors how to generate strong keys from biometrics and other noisy data"
    - "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.LO(Logic in Computer Science)