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: "private multiplicative weights beyond linear queries"
  Abstract: A wide variety of fundamental data analyses in machine learning, such as linear and logistic regression, require minimizing a convex function defined by the data. Since the data may contain sensitive information about individuals, and these analyses can leak that sensitive information, it is important to be able to solve convex minimization in a privacy-preserving way. #R##N#A series of recent results show how to accurately solve a single convex minimization problem in a differentially private manner. However, the same data is often analyzed repeatedly, and little is known about solving multiple convex minimization problems with differential privacy. For simpler data analyses, such as linear queries, there are remarkable differentially private algorithms such as the private multiplicative weights mechanism (Hardt and Rothblum, FOCS 2010) that accurately answer exponentially many distinct queries. In this work, we extend these results to the case of convex minimization and show how to give accurate and differentially private solutions to *exponentially many* convex minimization problems on a sensitive dataset.

Connected through hyperedge HE-1 (induced by source paper "private stochastic convex optimization with optimal rates"):
  with members:
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "generalization of erm in stochastic convex optimization the dimension strikes back"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "train faster generalize better stability of stochastic gradient descent"
    - "algorithmic stability for adaptive data analysis"
    - "preserving statistical validity in adaptive data analysis"

Connected through hyperedge HE-2 (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"
    - "linear queries estimation with local differential privacy"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "privacy amplification by iteration"):
  with members:
    - "blender enabling local search with a hybrid differential privacy model"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "renyi differential privacy"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "communication efficient learning of deep networks from decentralized data"
    - "concentrated differential privacy"
    - "simultaneous private learning of multiple concepts"

Connected through hyperedge HE-4 (induced by source paper "tight lower bounds for locally differentially private selection"):
  with members:
    - "the price of selection in differential privacy"
    - "tight lower bounds for differentially private selection"
    - "heavy hitters and the structure of local privacy"
    - "distributed private heavy hitters"
    - "calibrating data to sensitivity in private data analysis"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-5 (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"
    - "privacy via the johnson lindenstrauss transform"

Connected through hyperedge HE-6 (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"
    - "interactive fingerprinting codes and the hardness of preventing false discovery"
    - "the geometry of differential privacy the sparse and approximate cases"

Connected through hyperedge HE-7 (induced by source paper "calibration for the computationally identifiable masses"):
  with members:
    - "introduction to online convex optimization"
    - "fairness through computationally bounded awareness"
    - "on fairness and calibration"
    - "a convex framework for fair regression"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "calibrated fairness in bandits"
    - "ranking with fairness constraints"
    - "fairness in learning classic and contextual bandits"

Connected through hyperedge HE-8 (induced by source paper "the price of selection in differential privacy"):
  with members:
    - "algorithmic stability for adaptive data analysis"
    - "calibrating data to sensitivity in private data analysis"
    - "the power of linear reconstruction attacks"
    - "iterative constructions and private data release"
    - "interactive privacy via the median mechanism"

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)