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: "privately releasing conjunctions and the statistical query barrier"
  Abstract: Suppose we would like to know all answers to a set of statistical queries C on a data set up to small error, but we can only access the data itself using statistical queries. A trivial solution is to exhaustively ask all queries in C. Can we do any better? #R##N#+ We show that the number of statistical queries necessary and sufficient for this task is---up to polynomial factors---equal to the agnostic learning complexity of C in Kearns' statistical query (SQ) model. This gives a complete answer to the question when running time is not a concern. #R##N#+ We then show that the problem can be solved efficiently (allowing arbitrary error on a small fraction of queries) whenever the answers to C can be described by a submodular function. This includes many natural concept classes, such as graph cuts and Boolean disjunctions and conjunctions. #R##N#While interesting from a learning theoretic point of view, our main applications are in privacy-preserving data analysis: #R##N#Here, our second result leads to the first algorithm that efficiently releases differentially private answers to of all Boolean conjunctions with 1% average error. This presents significant progress on a key open problem in privacy-preserving data analysis. #R##N#Our first result on the other hand gives unconditional lower bounds on any differentially private algorithm that admits a (potentially non-privacy-preserving) implementation using only statistical queries. Not only our algorithms, but also most known private algorithms can be implemented using only statistical queries, and hence are constrained by these lower bounds. Our result therefore isolates the complexity of agnostic learning in the SQ-model as a new barrier in the design of differentially private algorithms.

Connected through hyperedge HE-1 (induced by source paper "approximate mathbb f _2 sketching of valuation functions"):
  with members:
    - "optimality of linear sketching under modular updates"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "testing submodularity and other properties of valuation functions"
    - "almost optimal streaming algorithms for coverage problems"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "towards tight bounds for the streaming set cover problem"
    - "streaming algorithms for submodular function maximization"
    - "incidence geometries and the pass complexity of semi streaming set cover"

Connected through hyperedge HE-2 (induced by source paper "data dependent differentially private parameter learning for directed graphical models"):
  with members:
    - "data dependent pac bayes priors via differential privacy"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "differentially private bayesian inference for exponential families"
    - "differentially private learning of undirected graphical models using collective graphical models"
    - "dp em differentially private expectation maximization"
    - "on the theory and practice of privacy preserving bayesian data analysis"
    - "understanding deep learning requires rethinking generalization"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"

Connected through hyperedge HE-3 (induced by source paper "the power of factorization mechanisms in local and central differential privacy"):
  with members:
    - "differentially private sql with bounded user contribution"
    - "optimizing error of high dimensional statistical queries under differential privacy"
    - "towards instance optimal private query release"
    - "towards practical differential privacy for sql queries"
    - "heavy hitters and the structure of local privacy"
    - "prochlo strong privacy for analytics in the crowd"
    - "an improved private mechanism for small databases"
    - "rappor randomized aggregatable privacy preserving ordinal response"

Connected through hyperedge HE-4 (induced by source paper "graphical model based estimation and inference for differential privacy"):
  with members:
    - "optimizing error of high dimensional statistical queries under differential privacy"
    - "cipher construction of differentially private microdata from low dimensional histograms via solving linear equations with tikhonov regularization"
    - "differentially private learning of undirected graphical models using collective graphical models"
    - "a data and workload aware algorithm for range queries under differential privacy"
    - "dual query practical private query release for high dimensional data"
    - "calibrating data to sensitivity in private data analysis"
    - "faster algorithms for privately releasing marginals"
    - "differentially private grids for geospatial data"

Connected through hyperedge HE-5 (induced by source paper "the complexity of partial function extension for coverage functions"):
  with members:
    - "partial function extension with applications to learning and property testing"
    - "the limitations of optimization from samples"
    - "maximizing social influence in nearly optimal time"
    - "general truthfulness characterizations via convex analysis"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-6 (induced by source paper "how to use heuristics for differential privacy"):
  with members:
    - "unleashing linear optimizers for group fair learning and optimization"
    - "model agnostic private learning via stability"
    - "property testing for differential privacy"
    - "private pac learning implies finite littlestone dimension"
    - "privacy preserving prediction"
    - "online learning via differential privacy"
    - "adaptive learning with robust generalization guarantees"
    - "efficient algorithms for adversarial contextual learning"

Connected through hyperedge HE-7 (induced by source paper "spectral sparsification of hypergraphs"):
  with members:
    - "cheeger inequalities for submodular transformations"
    - "sketching cuts in graphs and hypergraphs"
    - "hypergraph markov operators eigenvalues and approximation algorithms"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "learning pseudo boolean k dnf and submodular functions"
    - "submodular functions are noise stable"
    - "fast approximation algorithms for cut based problems in undirected graphs"
    - "twice ramanujan sparsifiers"

Connected through hyperedge HE-8 (induced by source paper "marginal release under local differential privacy"):
  with members:
    - "collecting and analyzing data from smart device users with local differential privacy"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "analysis of boolean functions"
    - "differentially private multi party computation optimality of non interactive randomized response"
    - "extremal mechanisms for local differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "faster algorithms for privately releasing marginals"

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)