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: "beyond worst case analysis in private singular vector computation"
  Abstract: We consider differentially private approximate singular vector computation. Known worst-case lower bounds show that the error of any differentially private algorithm must scale polynomially with the dimension of the singular vector. We are able to replace this dependence on the dimension by a natural parameter known as the coherence of the matrix that is often observed to be significantly smaller than the dimension both theoretically and empirically. We also prove a matching lower bound showing that our guarantee is nearly optimal for every setting of the coherence parameter. Notably, we achieve our bounds by giving a robust analysis of the well-known power iteration algorithm, which may be of independent interest. Our algorithm also leads to improvements in worst-case settings and to better low-rank approximations in the spectral norm.

Connected through hyperedge HE-1 (induced by source paper "pd ml lite private distributed machine learning from lighweight cryptography"):
  with members:
    - "a flexible privacy preserving framework for singular value decomposition under internet of things environment"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "typicality based stability and privacy"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "fast differentially private matrix factorization"
    - "a new approach to practical active secure two party computation"
    - "distributed learning communication complexity and privacy"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-2 (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-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"
    - "beating randomized response on incoherent matrices"
    - "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"
    - "beating randomized response on incoherent matrices"

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"
    - "beating randomized response on incoherent matrices"
    - "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 "clustering under local stability bridging the gap between worst case and beyond worst case analysis"):
  with members:
    - "k center clustering under perturbation resilience"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "decompositions of triangle dense graphs"

Connected through hyperedge HE-7 (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-8 (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"

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)