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: "linear queries estimation with local differential privacy"
  Abstract: We study the problem of estimating a set of $d$ linear queries with respect to some unknown distribution $\mathbf{p}$ over a domain $\mathcal{J}=[J]$ based on a sensitive data set of $n$ individuals under the constraint of local differential privacy. This problem subsumes a wide range of estimation tasks, e.g., distribution estimation and $d$-dimensional mean estimation. We provide new algorithms for both the offline (non-adaptive) and adaptive versions of this problem. #R##N#In the offline setting, the set of queries are fixed before the algorithm starts. In the regime where $n\lesssim d^2/\log(J)$, our algorithms attain $L_2$ estimation error that is independent of $d$, and is tight up to a factor of $\tilde{O}\left(\log^{1/4}(J)\right)$. For the special case of distribution estimation, we show that projecting the output estimate of an algorithm due to [Acharya et al. 2018] on the probability simplex yields an $L_2$ error that depends only sub-logarithmically on $J$ in the regime where $n\lesssim J^2/\log(J)$. These results show the possibility of accurate estimation of linear queries in the high-dimensional settings under the $L_2$ error criterion. #R##N#In the adaptive setting, the queries are generated over $d$ rounds; one query at a time. In each round, a query can be chosen adaptively based on all the history of previous queries and answers. We give an algorithm for this problem with optimal $L_{\infty}$ estimation error (worst error in the estimated values for the queries w.r.t. the data distribution). Our bound matches a lower bound on the $L_{\infty}$ error for the offline version of this problem [Duchi et al. 2013].

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"
    - "ektelo a framework for defining differentially private computations"
    - "practical locally private heavy hitters"

Connected through hyperedge HE-2 (induced by source paper "wireless federated learning with local differential privacy"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "differential privacy enabled federated learning for sensitive health data"
    - "federated learning with differential privacy algorithms and performance analysis"
    - "federated learning for edge networks resource optimization and incentive mechanism"
    - "hierarchical federated learning across heterogeneous cellular networks"
    - "federated learning over wireless fading channels"
    - "federated learning with bayesian differential privacy"

Connected through hyperedge HE-3 (induced by source paper "communication complexity in locally private distribution estimation and heavy hitters"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "detecting correlations with little memory and communication"
    - "distributed simulation and distributed inference"
    - "test without trust optimal locally private distribution testing"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "practical locally private heavy hitters"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"

Connected through hyperedge HE-4 (induced by source paper "locally differentially private data collection and analysis"):
  with members:
    - "collecting and analyzing multidimensional data with local differential privacy"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "locally private gaussian estimation"
    - "locally private mean estimation z test and tight confidence intervals"
    - "differentially private mixture of generative neural networks"
    - "locally differentially private heavy hitter identification"
    - "renyi differential privacy"
    - "heavy hitters and the structure of local privacy"

Connected through hyperedge HE-5 (induced by source paper "locally differentially private frequency estimation with consistency"):
  with members:
    - "estimating numerical distributions under local differential privacy"
    - "pckv locally differentially private correlated key value data collection with optimized utility"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "calibrate frequency estimation and heavy hitter identification with local differential privacy via incorporating prior knowledge"
    - "local differential privacy for evolving data"
    - "practical locally private heavy hitters"
    - "locally differentially private heavy hitter identification"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"

Connected through hyperedge HE-6 (induced by source paper "estimating smooth glm in non interactive local differential privacy model with public unlabeled data"):
  with members:
    - "privately answering classification queries in the agnostic pac model"
    - "learning without interaction requires separation"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "noninteractive locally private learning of linear models via polynomial approximations"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "collecting telemetry data privately"
    - "learning privately from multiparty data"
    - "rappor randomized aggregatable privacy preserving ordinal response"

Connected through hyperedge HE-7 (induced by source paper "price of privacy in the keynesian beauty contest"):
  with members:
    - "collecting and analyzing multidimensional data with local differential privacy"
    - "locally private gaussian estimation"
    - "perception games and privacy"
    - "extremal mechanisms for local differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "truthful mechanisms for agents that value privacy"
    - "privacy aware mechanism design"
    - "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.LG(Machine Learning)