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: "universally utility maximizing privacy mechanisms"
  Abstract: A mechanism for releasing information about a statistical database with sensitive data must resolve a trade-off between utility and privacy. Privacy can be rigorously quantified using the framework of {\em differential privacy}, which requires that a mechanism's output distribution is nearly the same whether or not a given database row is included or excluded. The goal of this paper is strong and general utility guarantees, subject to differential privacy. #R##N#We pursue mechanisms that guarantee near-optimal utility to every potential user, independent of its side information (modeled as a prior distribution over query results) and preferences (modeled via a loss function). #R##N#Our main result is: for each fixed count query and differential privacy level, there is a {\em geometric mechanism} $M^*$ -- a discrete variant of the simple and well-studied Laplace mechanism -- that is {\em simultaneously expected loss-minimizing} for every possible user, subject to the differential privacy constraint. This is an extremely strong utility guarantee: {\em every} potential user $u$, no matter what its side information and preferences, derives as much utility from $M^*$ as from interacting with a differentially private mechanism $M_u$ that is optimally tailored to $u$.

Connected through hyperedge HE-1 (induced by source paper "guidelines for implementing and auditing differentially private systems"):
  with members:
    - "differentially private objective perturbation beyond smoothness and convexity"
    - "identifying cache based side channels through secret augmented abstract interpretation"
    - "fuzzi a three level logic for differential privacy"
    - "an economic analysis of privacy protection and statistical accuracy as social choices"
    - "private selection from private candidates"
    - "ektelo a framework for defining differentially private computations"
    - "detecting violations of differential privacy"
    - "how to use heuristics for differential privacy"

Connected through hyperedge HE-2 (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"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "synthesizing coupling proofs of differential privacy"
    - "renyi differential privacy"
    - "towards practical differential privacy for sql queries"

Connected through hyperedge HE-3 (induced by source paper "drynx decentralized secure verifiable system for statistical queries and machine learning on distributed datasets"):
  with members:
    - "helen maliciously secure coopetitive learning for linear models"
    - "evaluating differentially private machine learning in practice"
    - "hotstuff bft consensus in the lens of blockchain"
    - "dp admm admm based distributed learning with differential privacy"
    - "gazelle a low latency framework for secure neural network inference"
    - "plausible deniability for privacy preserving data synthesis"
    - "differentially private federated learning a client level perspective"
    - "towards practical differential privacy for sql queries"

Connected through hyperedge HE-4 (induced by source paper "differentially private anonymized histograms"):
  with members:
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "inspectre privately estimating the unseen"
    - "efficient lipschitz extensions for high dimensional graph statistics and node private degree distributions"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "minimax estimation of functionals of discrete distributions"
    - "extremal mechanisms for local differential privacy"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"
    - "estimating renyi entropy of discrete distributions"

Connected through hyperedge HE-5 (induced by source paper "privacy and utility preserving sensor data transformations"):
  with members:
    - "mobile sensor data anonymization"
    - "towards federated learning at scale system design"
    - "replacement autoencoder a privacy preserving algorithm for sensory data analysis"
    - "protecting sensory data against sensitive inferences"
    - "context aware generative adversarial privacy"
    - "prochlo strong privacy for analytics in the crowd"
    - "learning human identity from motion patterns"
    - "censoring representations with an adversary"

Connected through hyperedge HE-6 (induced by source paper "location trace privacy under conditional priors"):
  with members:
    - "composition properties of inferential privacy for time series data"
    - "renyi differential privacy"
    - "protecting locations with differential privacy under temporal correlations"
    - "geo indistinguishability differential privacy for location based systems"
    - "interactive privacy via the median mechanism"

Connected through hyperedge HE-7 (induced by source paper "elliptical perturbations for differential privacy"):
  with members:
    - "benefits and pitfalls of the exponential mechanism with applications to hilbert spaces and functional pca"
    - "the bernstein mechanism function release under differential privacy"
    - "functional mechanism regression analysis under differential privacy"
    - "on the geometry of differential privacy"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-8 (induced by source paper "mobilitymirror bias adjusted transportation datasets"):
  with members:
    - "bias in olap queries detection explanation and removal"
    - "fairness testing testing software for discrimination"
    - "discrimination in online ad delivery"
    - "calibrating data to sensitivity in private data analysis"
    - "dpcube differentially private histogram release through multidimensional partitioning"
    - "differentially private publication of sparse data"
    - "differential privacy via wavelet transforms"
    - "boosting the accuracy of differentially private histograms through consistency"

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)