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: "between pure and approximate differential privacy"
  Abstract: We show a new lower bound on the sample complexity of $(\varepsilon, \delta)$-differentially private algorithms that accurately answer statistical queries on high-dimensional databases. The novelty of our bound is that it depends optimally on the parameter $\delta$, which loosely corresponds to the probability that the algorithm fails to be private, and is the first to smoothly interpolate between approximate differential privacy ($\delta > 0$) and pure differential privacy ($\delta = 0$). #R##N#Specifically, we consider a database $D \in \{\pm1\}^{n \times d}$ and its \emph{one-way marginals}, which are the $d$ queries of the form "What fraction of individual records have the $i$-th bit set to $+1$?" We show that in order to answer all of these queries to within error $\pm \alpha$ (on average) while satisfying $(\varepsilon, \delta)$-differential privacy, it is necessary that $$ n \geq \Omega\left( \frac{\sqrt{d \log(1/\delta)}}{\alpha \varepsilon} \right), $$ which is optimal up to constant factors. To prove our lower bound, we build on the connection between \emph{fingerprinting codes} and lower bounds in differential privacy (Bun, Ullman, and Vadhan, STOC'14). #R##N#In addition to our lower bound, we give new purely and approximately differentially private algorithms for answering arbitrary statistical queries that improve on the sample complexity of the standard Laplace and Gaussian mechanisms for achieving worst-case accuracy guarantees by a logarithmic factor.

Connected through hyperedge HE-1 (induced by source paper "privately learning markov random fields"):
  with members:
    - "lower bounds for testing graphical models colorings and antiferromagnetic ising models"
    - "private hypothesis selection"
    - "data dependent differentially private parameter learning for directed graphical models"
    - "graphical model based estimation and inference for differential privacy"
    - "privately learning high dimensional distributions"
    - "sparse logistic regression learns all discrete pairwise graphical models"
    - "inspectre privately estimating the unseen"
    - "learning graphical models using multiplicative weights"

Connected through hyperedge HE-2 (induced by source paper "pure differentially private summation from anonymous messages"):
  with members:
    - "on the power of multiple anonymous messages"
    - "improved summation from shuffling"
    - "separating local shuffled differential privacy via histograms"
    - "the privacy blanket of the shuffle model"
    - "differentially private summation with multi message shuffling"
    - "private aggregation from fewer anonymous messages"
    - "scalable and differentially private distributed aggregation in the shuffled model"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-3 (induced by source paper "private mean estimation of heavy tailed distributions"):
  with members:
    - "private hypothesis selection"
    - "the optimal approximation factor in density estimation"
    - "differentially private algorithms for learning mixtures of separated gaussians"
    - "privately learning high dimensional distributions"
    - "locally private gaussian estimation"
    - "inspectre privately estimating the unseen"
    - "being robust in high dimensions can be practical"
    - "differentially private testing of identity and closeness of discrete distributions"

Connected through hyperedge HE-4 (induced by source paper "private hypothesis selection"):
  with members:
    - "the optimal approximation factor in density estimation"
    - "differentially private algorithms for learning mixtures of separated gaussians"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "privately learning high dimensional distributions"
    - "locally private gaussian estimation"
    - "private pac learning implies finite littlestone dimension"
    - "learning sums of independent random variables with sparse collective support"
    - "inspectre privately estimating the unseen"

Connected through hyperedge HE-5 (induced by source paper "private identity testing for high dimensional distributions"):
  with members:
    - "lower bounds for testing graphical models colorings and antiferromagnetic ising models"
    - "efficiently estimating erdos renyi graphs with node differential privacy"
    - "improved differentially private analysis of variance"
    - "individual sensitivity preprocessing for data privacy"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "learning and testing causal models with interventions"
    - "privately learning high dimensional distributions"
    - "test without trust optimal locally private distribution testing"

Connected through hyperedge HE-6 (induced by source paper "on the power of multiple anonymous messages"):
  with members:
    - "improved summation from shuffling"
    - "separating local shuffled differential privacy via histograms"
    - "the privacy blanket of the shuffle model"
    - "differentially private summation with multi message shuffling"
    - "practical and robust privacy amplification with multi party differential privacy"
    - "communication complexity in locally private distribution estimation and heavy hitters"
    - "private aggregation from fewer anonymous messages"
    - "scalable and differentially private distributed aggregation in the shuffled model"

Connected through hyperedge HE-7 (induced by source paper "differentially private algorithms for learning mixtures of separated gaussians"):
  with members:
    - "private hypothesis selection"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "privately learning high dimensional distributions"
    - "differentially private k means with constant multiplicative error"
    - "inspectre privately estimating the unseen"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"
    - "finite sample differentially private confidence intervals"
    - "clustering algorithms for the centralized and local models"

Connected through hyperedge HE-8 (induced by source paper "the cost of a reductions approach to private fair optimization"):
  with members:
    - "unleashing linear optimizers for group fair learning and optimization"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "privacy amplification by iteration"
    - "a reductions approach to fair classification"
    - "multi objective bandits optimizing the generalized gini index"
    - "equality of opportunity in supervised learning"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "concentrated differential privacy"

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.DS(Data Structures and Algorithms)