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: "the power of linear reconstruction attacks"
  Abstract: We consider the power of linear reconstruction attacks in statistical data privacy, showing that they can be applied to a much wider range of settings than previously understood. Linear attacks have been studied before (Dinur and Nissim PODS'03, Dwork, McSherry and Talwar STOC'07, Kasiviswanathan, Rudelson, Smith and Ullman STOC'10, De TCC'12, Muthukrishnan and Nikolov STOC'12) but have so far been applied only in settings with releases that are obviously linear. #R##N#Consider a database curator who manages a database of sensitive information but wants to release statistics about how a sensitive attribute (say, disease) in the database relates to some nonsensitive attributes (e.g., postal code, age, gender, etc). We show one can mount linear reconstruction attacks based on any release that gives: a) the fraction of records that satisfy a given non-degenerate boolean function. Such releases include contingency tables (previously studied by Kasiviswanathan et al., STOC'10) as well as more complex outputs like the error rate of classifiers such as decision trees; b) any one of a large class of M-estimators (that is, the output of empirical risk minimization algorithms), including the standard estimators for linear and logistic regression. #R##N#We make two contributions: first, we show how these types of releases can be transformed into a linear format, making them amenable to existing polynomial-time reconstruction algorithms. This is already perhaps surprising, since many of the above releases (like M-estimators) are obtained by solving highly nonlinear formulations. Second, we show how to analyze the resulting attacks under various distributional assumptions on the data. Specifically, we consider a setting in which the same statistic (either a) or b) above) is released about how the sensitive attribute relates to all subsets of size k (out of a total of d) nonsensitive boolean attributes.

Connected through hyperedge HE-1 (induced by source paper "gamin an adversarial approach to black box model inversion"):
  with members:
    - "high fidelity extraction of neural network models"
    - "evaluating differentially private machine learning in practice"
    - "mixmatch a holistic approach to semi supervised learning"
    - "exploiting unintended feature leakage in collaborative learning"
    - "csi neural network using side channels to recover your artificial neural network information"
    - "sok security and privacy in machine learning"
    - "stealing hyperparameters in machine learning"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "the advantages of multiple classes for reducing overfitting from test set reuse"):
  with members:
    - "do imagenet classifiers generalize to imagenet"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "the ladder a reliable leaderboard for machine learning competitions"
    - "generalization in adaptive data analysis and holdout reuse"
    - "algorithmic stability for adaptive data analysis"
    - "preventing false discovery in interactive data analysis is hard"
    - "imagenet large scale visual recognition challenge"
    - "preserving statistical validity in adaptive data analysis"

Connected through hyperedge HE-3 (induced by source paper "averaging attacks on bounded noise based disclosure control algorithms"):
  with members:
    - "diffix birch extending diffix aspen"
    - "linear program reconstruction in practice"
    - "when the signal is in the noise exploiting diffix s sticky noise"
    - "privacy assessment of de identified opal data a report for transport for nsw"
    - "differentially private release of public transport data the opal use case"
    - "concentrated differential privacy"
    - "between pure and approximate differential privacy"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-4 (induced by source paper "private incremental regression"):
  with members:
    - "private matchings and allocations"
    - "private approximations of the 2nd moment matrix using existing techniques in linear regression"
    - "sketching as a tool for numerical linear algebra"
    - "randomness efficient fast johnson lindenstrauss transform with applications in differential privacy and compressed sensing"
    - "private empirical risk minimization beyond the worst case the effect of the constraint set geometry"
    - "differentially private empirical risk minimization efficient algorithms and tight error bounds"
    - "circulant matrices and differential privacy"
    - "private multiplicative weights beyond linear queries"

Connected through hyperedge HE-5 (induced by source paper "tight lower bounds for differentially private selection"):
  with members:
    - "the price of selection in differential privacy"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "make up your mind the price of online queries in differential privacy"
    - "between pure and approximate differential privacy"
    - "algorithmic stability for adaptive data analysis"
    - "preventing false discovery in interactive data analysis is hard"
    - "private multiplicative weights beyond linear queries"
    - "interactive fingerprinting codes and the hardness of preventing false discovery"

Connected through hyperedge HE-6 (induced by source paper "privacy risk in machine learning analyzing the connection to overfitting"):
  with members:
    - "machine learning models that remember too much"
    - "understanding deep learning requires rethinking generalization"
    - "membership inference attacks against machine learning models"
    - "data dependent initializations of convolutional neural networks"
    - "revisiting differentially private regression lessons from learning theory and their consequences"
    - "generalization in adaptive data analysis and holdout reuse"
    - "algorithmic stability for adaptive data analysis"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "the price of selection in differential privacy"):
  with members:
    - "algorithmic stability for adaptive data analysis"
    - "private multiplicative weights beyond linear queries"
    - "calibrating data to sensitivity in private data analysis"
    - "iterative constructions and private data release"
    - "interactive privacy via the median mechanism"

Connected through hyperedge HE-8 (induced by source paper "on the differential privacy of bayesian inference"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "convergence rates for differentially private statistical estimation"
    - "functional mechanism regression analysis under differential privacy"
    - "bayesian inference under differential privacy"
    - "a learning theory approach to non interactive database privacy"
    - "differentially private empirical risk minimization"
    - "learning in a large function space privacy preserving mechanisms for svm learning"

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)