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: "privacy loss in apple s implementation of differential privacy on macos 10 12"
  Abstract: In June 2016, Apple announced that it will deploy differential privacy for some user data collection in order to ensure privacy of user data, even from Apple. The details of Apple's approach remained sparse. Although several patents have since appeared hinting at the algorithms that may be used to achieve differential privacy, they did not include a precise explanation of the approach taken to privacy parameter choice. Such choice and the overall approach to privacy budget use and management are key questions for understanding the privacy protections provided by any deployment of differential privacy. #R##N#In this work, through a combination of experiments, static and dynamic code analysis of macOS Sierra (Version 10.12) implementation, we shed light on the choices Apple made for privacy budget management. We discover and describe Apple's set-up for differentially private data processing, including the overall data pipeline, the parameters used for differentially private perturbation of each piece of data, and the frequency with which such data is sent to Apple's servers. #R##N#We find that although Apple's deployment ensures that the (differential) privacy loss per each datum submitted to its servers is $1$ or $2$, the overall privacy loss permitted by the system is significantly higher, as high as $16$ per day for the four initially announced applications of Emojis, New words, Deeplinks and Lookup Hints. Furthermore, Apple renews the privacy budget available every day, which leads to a possible privacy loss of 16 times the number of days since user opt-in to differentially private data collection for those four applications. #R##N#We advocate that in order to claim the full benefits of differentially private data collection, Apple must give full transparency of its implementation, enable user choice in areas related to privacy loss, and set meaningful defaults on the privacy loss permitted.

Connected through hyperedge HE-1 (induced by source paper "encode shuffle analyze privacy revisited formalizations and empirical evaluation"):
  with members:
    - "private heavy hitters and range queries in the shuffled model"
    - "the privacy blanket of the shuffle model"
    - "that which we call private"
    - "private stochastic convex optimization with optimal rates"
    - "evaluating differentially private machine learning in practice"
    - "differentially private summation with multi message shuffling"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "privacy amplification by subsampling tight analyses via couplings and divergences"

Connected through hyperedge HE-2 (induced by source paper "local information privacy and its application to privacy preserving data aggregation"):
  with members:
    - "context aware data aggregation with localized information privacy"
    - "practical locally private heavy hitters"
    - "context aware generative adversarial privacy"
    - "privacy aware mmse estimation"
    - "differential privacy as a mutual information constraint"
    - "concentrated differential privacy"
    - "privacy preserving analysis of correlated data"
    - "on maximal correlation mutual information and data privacy"

Connected through hyperedge HE-3 (induced by source paper "a blockchain based approach for saving and tracking differential privacy cost"):
  with members:
    - "zebralancer private and anonymous crowdsourcing system atop open blockchain"
    - "collecting telemetry data privately"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "concentrated differential privacy"
    - "generalization in adaptive data analysis and holdout reuse"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "the composition theorem for differential privacy"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-4 (induced by source paper "ara aggregated rappor and analysis for centralized differential privacy"):
  with members:
    - "outis crypto assisted differential privacy on untrusted servers"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "prochlo strong privacy for analytics in the crowd"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "what can we learn privately"

Connected through hyperedge HE-5 (induced by source paper "evaluating differentially private machine learning in practice"):
  with members:
    - "differentially private model publishing for deep learning"
    - "privacy preserving collaborative deep learning with irregular participants"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "dp admm admm based distributed learning with differential privacy"
    - "how to backdoor federated learning"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "efficient deep learning on multi source private data"

Connected through hyperedge HE-6 (induced by source paper "the power of synergy in differential privacy combining a small curator with local randomizers"):
  with members:
    - "locally private k means clustering"
    - "blender enabling local search with a hybrid differential privacy model"
    - "the privacy blanket of the shuffle model"
    - "differentially private summation with multi message shuffling"
    - "exponential separations in local differential privacy through communication complexity"
    - "scalable and differentially private distributed aggregation in the shuffled model"
    - "the role of interactivity in local differential privacy"
    - "amplification by shuffling from local to central differential privacy via anonymity"

Connected through hyperedge HE-7 (induced by source paper "free gap information from the differentially private sparse vector and noisy max mechanisms"):
  with members:
    - "proving differential privacy with shadow execution"
    - "private selection from private candidates"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "ektelo a framework for defining differentially private computations"
    - "synthesizing coupling proofs of differential privacy"
    - "renyi differential privacy"
    - "towards practical differential privacy for sql queries"
    - "prochlo strong privacy for analytics in the crowd"

Connected through hyperedge HE-8 (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"
    - "renyi differential privacy"
    - "achieving secure and differentially private computations in multiparty settings"
    - "collecting telemetry data privately"
    - "differential privacy as a mutual information constraint"
    - "mutual information optimally local private discrete distribution estimation"

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)