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: "utility optimized local differential privacy mechanisms for distribution estimation"
  Abstract: LDP (Local Differential Privacy) has been widely studied to estimate statistics of personal data (e.g., distribution underlying the data) while protecting users' privacy. Although LDP does not require a trusted third party, it regards all personal data equally sensitive, which causes excessive obfuscation hence the loss of utility. In this paper, we introduce the notion of ULDP (Utility-optimized LDP), which provides a privacy guarantee equivalent to LDP only for sensitive data. We first consider the setting where all users use the same obfuscation mechanism, and propose two mechanisms providing ULDP: utility-optimized randomized response and utility-optimized RAPPOR. We then consider the setting where the distinction between sensitive and non-sensitive data can be different from user to user. For this setting, we propose a personalized ULDP mechanism with semantic tags to estimate the distribution of personal data with high utility while keeping secret what is sensitive for each user. We show theoretically and experimentally that our mechanisms provide much higher utility than the existing LDP mechanisms when there are a lot of non-sensitive data. We also show that when most of the data are non-sensitive, our mechanisms even provide almost the same utility as non-private mechanisms in the low privacy regime.

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"
    - "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"
    - "stealing machine learning models via prediction apis"

Connected through hyperedge HE-2 (induced by source paper "linear and range counting under metric based local differential privacy"):
  with members:
    - "the privacy blanket of the shuffle model"
    - "secure and utility aware data collection with condensed local differential privacy"
    - "consistent and accurate frequency oracles under local differential privacy"
    - "capacity bounded differential privacy"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "distributed differential privacy via shuffling"
    - "practical locally private heavy hitters"

Connected through hyperedge HE-3 (induced by source paper "practical and robust privacy amplification with multi party differential privacy"):
  with members:
    - "outis crypto assisted differential privacy on untrusted servers"
    - "separating local shuffled differential privacy via histograms"
    - "private heavy hitters and range queries in the shuffled model"
    - "the privacy blanket of the shuffle model"
    - "secure and utility aware data collection with condensed local differential privacy"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "an algorithmic framework for differentially private data analysis on trusted processors"

Connected through hyperedge HE-4 (induced by source paper "secure and utility aware data collection with condensed local differential privacy"):
  with members:
    - "blender enabling local search with a hybrid differential privacy model"
    - "detecting violations of differential privacy"
    - "practical locally private heavy hitters"
    - "marginal release under local differential privacy"
    - "heavy hitters and the structure of local privacy"
    - "collecting telemetry data privately"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "local private efficient protocols for succinct histograms"

Connected through hyperedge HE-5 (induced by source paper "local obfuscation mechanisms for hiding probability distributions"):
  with members:
    - "local distribution obfuscation via probability coupling"
    - "profile based privacy for locally private computations"
    - "generalised differential privacy for text document processing"
    - "on the compositionality of quantitative information flow"
    - "pufferfish privacy mechanisms for correlated data"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "optimal geo indistinguishable mechanisms for location privacy"
    - "differential privacy an economic method for choosing epsilon"

Connected through hyperedge HE-6 (induced by source paper "privacy preserving multiple tensor factorization for synthesizing large scale location traces"):
  with members:
    - "local obfuscation mechanisms for hiding probability distributions"
    - "on the lack of location privacy in crowdsourcing applications"
    - "the long road to computational location privacy a survey"
    - "evaluating differentially private machine learning in practice"
    - "plausible deniability for privacy preserving data synthesis"
    - "fast differentially private matrix factorization"
    - "time distortion anonymization for the publication of mobility data with high utility"
    - "algorithms initializations and convergence for the nonnegative matrix factorization"

Connected through hyperedge HE-7 (induced by source paper "local distribution obfuscation via probability coupling"):
  with members:
    - "local obfuscation mechanisms for hiding probability distributions"
    - "sok differential privacies"
    - "profile based privacy for locally private computations"
    - "renyi differential privacy"
    - "on the compositionality of quantitative information flow"
    - "differential privacy as a mutual information constraint"
    - "concentrated differential privacy"
    - "rappor randomized aggregatable privacy preserving ordinal response"

Connected through hyperedge HE-8 (induced by source paper "providing input discriminative protection for local differential privacy"):
  with members:
    - "context aware data aggregation with localized information privacy"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "blowfish privacy tuning privacy utility trade offs using policies"
    - "geo indistinguishability differential privacy for location based systems"

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.DB(Databases)