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: "extremal mechanisms for local differential privacy"
  Abstract: Local differential privacy has recently surfaced as a strong measure of privacy in contexts where personal information remains private even from data analysts. Working in a setting where both the data providers and data analysts want to maximize the utility of statistical analyses performed on the released data, we study the fundamental trade-off between local differential privacy and utility. This trade-off is formulated as a constrained optimization problem: maximize utility subject to local differential privacy constraints. We introduce a combinatorial family of extremal privatization mechanisms, which we call staircase mechanisms, and show that it contains the optimal privatization mechanisms for a broad class of information theoretic utilities such as mutual information and $f$-divergences. We further prove that for any utility function and any privacy level, solving the privacy-utility maximization problem is equivalent to solving a finite-dimensional linear program, the outcome of which is the optimal staircase mechanism. However, solving this linear program can be computationally expensive since it has a number of variables that is exponential in the size of the alphabet the data lives in. To account for this, we show that two simple privatization mechanisms, the binary and randomized response mechanisms, are universally optimal in the low and high privacy regimes, and well approximate the intermediate regime.

Connected through hyperedge HE-1 (induced by source paper "a workload adaptive mechanism for linear queries under local differential privacy"):
  with members:
    - "graphical model based estimation and inference for differential privacy"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "the power of factorization mechanisms in local and central differential privacy"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "optimizing error of high dimensional statistical queries under differential privacy"
    - "towards instance optimal private query release"
    - "linear queries estimation with local differential privacy"
    - "ektelo a framework for defining differentially private computations"

Connected through hyperedge HE-2 (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-3 (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"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "practical privacy preserving poi recommendation"):
  with members:
    - "semi supervised learning meets factorization learning to recommend with chain graph model"
    - "privacy preserving point of interest recommendation using decentralized matrix factorization"
    - "secure social recommendation based on secret sharing"
    - "federated learning strategies for improving communication efficiency"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "distributed collaborative hashing and its applications in ant financial"
    - "neural collaborative filtering"
    - "deepfm a factorization machine based neural network for ctr prediction"

Connected through hyperedge HE-5 (induced by source paper "privacy preserving boosting in the local setting"):
  with members:
    - "collecting and analyzing multidimensional data with local differential privacy"
    - "locally differentially private naive bayes classification"
    - "differentially private draw and discard machine learning"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"
    - "clustering algorithms for the centralized and local models"
    - "prochlo strong privacy for analytics in the crowd"
    - "collecting telemetry data privately"
    - "collecting and analyzing data from smart device users with local differential privacy"

Connected through hyperedge HE-6 (induced by source paper "input perturbation a new paradigm between central and local differential privacy"):
  with members:
    - "generalization in generative adversarial networks a novel perspective from privacy protection"
    - "efficiently estimating erdos renyi graphs with node differential privacy"
    - "differentially private bayesian linear regression"
    - "collecting and analyzing multidimensional data with local differential privacy"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "noninteractive locally private learning of linear models via polynomial approximations"
    - "efficient private erm for smooth objectives"
    - "concentrated differential privacy simplifications extensions and lower bounds"

Connected through hyperedge HE-7 (induced by source paper "fair learning with private demographic data"):
  with members:
    - "differential privacy has disparate impact on model accuracy"
    - "the cost of a reductions approach to private fair optimization"
    - "noise tolerant fair classification"
    - "training well generalizing classifiers for fairness metrics and other data dependent constraints"
    - "differentially private fair learning"
    - "a reductions approach to fair classification"
    - "proxy fairness"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-8 (induced by source paper "the privacy funnel from the viewpoint of local differential privacy"):
  with members:
    - "encode shuffle analyze privacy revisited formalizations and empirical evaluation"
    - "a submodularity based agglomerative clustering algorithm for the privacy funnel"
    - "information theoretic metrics for local differential privacy protocols"
    - "on perfect privacy and maximal correlation"
    - "principal inertia components and applications"
    - "differential privacy as a mutual information constraint"
    - "what can we learn privately"

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.IT(Information Theory)