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: "beating randomized response on incoherent matrices"
  Abstract: Computing accurate low rank approximations of large matrices is a fundamental data mining task. In many applications however the matrix contains sensitive information about individuals. In such case we would like to release a low rank approximation that satisfies a strong privacy guarantee such as differential privacy. Unfortunately, to date the best known algorithm for this task that satisfies differential privacy is based on naive input perturbation or randomized response: Each entry of the matrix is perturbed independently by a sufficiently large random noise variable, a low rank approximation is then computed on the resulting matrix. We give (the first) significant improvements in accuracy over randomized response under the natural and necessary assumption that the matrix has low coherence. Our algorithm is also very efficient and finds a constant rank approximation of an m x n matrix in time O(mn). Note that even generating the noise matrix required for randomized response already requires time O(mn).

Connected through hyperedge HE-1 (induced by source paper "mvg mechanism differential privacy under matrix valued query"):
  with members:
    - "a differential privacy mechanism design under matrix valued query"
    - "towards practical differential privacy for sql queries"
    - "coupling random orthonormal projection with gaussian generative model for non interactive private data release"
    - "convex optimization for linear query processing under approximate differential privacy"
    - "understanding the sparse vector technique for differential privacy"
    - "principled evaluation of differentially private algorithms using dpbench"
    - "optimizing batch linear queries under exact and approximate differential privacy"
    - "an improved private mechanism for small databases"

Connected through hyperedge HE-2 (induced by source paper "release connection fingerprints in social networks using personalized diffierential privacy"):
  with members:
    - "mining frequent graph patterns with differential privacy"
    - "preserving link privacy in social network based systems"

Connected through hyperedge HE-3 (induced by source paper "differentially private matrix completion revisited"):
  with members:
    - "fast matrix completion without the condition number"
    - "large scale multi label learning with missing labels"
    - "calibrating data to sensitivity in private data analysis"
    - "mechanism design in large games incentives and privacy"
    - "beyond worst case analysis in private singular vector computation"
    - "large scale convex minimization with a low rank constraint"
    - "guaranteed rank minimization via singular value projection"
    - "matrix completion from a few entries"

Connected through hyperedge HE-4 (induced by source paper "smooth sensitivity based approach for differentially private principal component analysis"):
  with members:
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "fast rates for empirical risk minimization of strict saddle problems"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "efficient frequent directions algorithm for sparse matrices"
    - "robust shift and invert preconditioning faster and more sample efficient algorithms for eigenvector computation"
    - "convergence of stochastic gradient descent for pca"
    - "low rank approximation and regression in input sparsity time"
    - "beyond worst case analysis in private singular vector computation"

Connected through hyperedge HE-5 (induced by source paper "the policies of designing differentially private mechanisms utility first vs privacy first"):
  with members:
    - "privacy preserving data analysis for the federal statistical agencies"
    - "calibrating data to sensitivity in private data analysis"
    - "beyond worst case analysis in private singular vector computation"
    - "iterative constructions and private data release"
    - "interactive privacy via the median mechanism"
    - "learning in a large function space privacy preserving mechanisms for svm learning"

Connected through hyperedge HE-6 (induced by source paper "the price of differential privacy for low rank factorization"):
  with members:
    - "communication efficient algorithms for distributed stochastic principal component analysis"
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "input sparsity time low rank approximation via ridge leverage score sampling"
    - "optimal principal component analysis in distributed and streaming models"
    - "wishart mechanism for differentially private principal components analysis"
    - "sketching as a tool for numerical linear algebra"
    - "differentially private linear algebra in the streaming model"
    - "dimensionality reduction for k means clustering and low rank approximation"

Connected through hyperedge HE-7 (induced by source paper "fast and space optimal low rank factorization in the streaming model with application in differential privacy"):
  with members:
    - "optimal principal component analysis in distributed and streaming models"
    - "wishart mechanism for differentially private principal components analysis"
    - "differentially private linear algebra in the streaming model"
    - "randomness efficient fast johnson lindenstrauss transform with applications in differential privacy and compressed sensing"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "the noisy power method a meta algorithm with applications"
    - "low rank approximation and regression in input sparsity time"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-8 (induced by source paper "on low space differentially private low rank factorization in the spectral norm"):
  with members:
    - "the price of differential privacy for low rank factorization"
    - "optimal principal component analysis in distributed and streaming models"
    - "wishart mechanism for differentially private principal components analysis"
    - "differentially private linear algebra in the streaming model"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "circulant matrices and differential privacy"
    - "the noisy power method a meta algorithm with applications"
    - "low rank approximation and regression in input sparsity time"

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)