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: "relative errors for deterministic low rank matrix approximations"
  Abstract: We consider processing an n x d matrix A in a stream with row-wise updates according to a recent algorithm called Frequent Directions (Liberty, KDD 2013). This algorithm maintains an l x d matrix Q deterministically, processing each row in O(d l^2) time; the processing time can be decreased to O(d l) with a slight modification in the algorithm and a constant increase in space. We show that if one sets l = k+ k/eps and returns Q_k, a k x d matrix that is the best rank k approximation to Q, then we achieve the following properties: ||A - A_k||_F^2 <= ||A||_F^2 - ||Q_k||_F^2 <= (1+eps) ||A - A_k||_F^2 and where pi_{Q_k}(A) is the projection of A onto the rowspace of Q_k then ||A - pi_{Q_k}(A)||_F^2 <= (1+eps) ||A - A_k||_F^2. #R##N#We also show that Frequent Directions cannot be adapted to a sparse version in an obvious way that retains the l original rows of the matrix, as opposed to a linear combination or sketch of the rows.

Connected through hyperedge HE-1 (induced by source paper "blinkml efficient maximum likelihood estimation with probabilistic guarantees"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "verdictdb universalizing approximate query processing"
    - "measuring the effects of data parallelism on neural network training"
    - "hemingway modeling distributed optimization algorithms"
    - "unbiased estimates for linear regression via volume sampling"
    - "database learning toward a database that becomes smarter every time"
    - "noscope optimizing neural network queries over video at scale"
    - "clipper a low latency online prediction serving system"

Connected through hyperedge HE-2 (induced by source paper "towards optimal moment estimation in streaming and distributed models"):
  with members:
    - "optimal random sampling from distributed streams revisited"
    - "weighted reservoir sampling from distributed streams"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "distributed statistical estimation of matrix products with applications"
    - "revisiting frequency moment estimation in random order streams"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "continuous monitoring of ell_p norms in data streams"
    - "communication optimal distributed clustering"

Connected through hyperedge HE-3 (induced by source paper "learning based low rank approximations"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "learning to branch"
    - "hyperspectral image dataset for benchmarking on salient object detection"
    - "competitive caching with machine learned advice"
    - "the case for learned index structures"
    - "learning combinatorial optimization algorithms over graphs"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "lazysvd even faster svd decomposition yet without agonizing pain"

Connected through hyperedge HE-4 (induced by source paper "federated pca with adaptive rank estimation"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "cola decentralized linear learning"
    - "moses a streaming algorithm for linear dimensionality reduction"
    - "differentially private federated learning a client level perspective"
    - "robust frequent directions with application in online learning"
    - "federated multi task learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "improved practical matrix sketching with guarantees"

Connected through hyperedge HE-5 (induced by source paper "leveraging well conditioned bases streaming distributed summaries in minkowski p norms"):
  with members:
    - "frequent directions simple and deterministic matrix sketching"
    - "sketching as a tool for numerical linear algebra"
    - "quantile regression for large scale applications"
    - "how robust are linear sketches to adaptive inputs"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "the fast cauchy transform and faster robust linear regression"
    - "on the complexity of processing massive unordered distributed data"

Connected through hyperedge HE-6 (induced by source paper "coresets and sketches"):
  with members:
    - "coresets for k means and k median clustering and their applications"
    - "optimal quantile approximation in streams"
    - "dimensionality reduction of massive sparse datasets using coresets"
    - "the optimal sample complexity of pac learning"
    - "input sparsity and hardness for robust subspace approximation"
    - "sparse approximation via generating point sets"
    - "frequent directions simple and deterministic matrix sketching"
    - "epsilon kernel coresets for stochastic points"

Connected through hyperedge HE-7 (induced by source paper "efficient frequent directions algorithm for sparse matrices"):
  with members:
    - "improved practical matrix sketching with guarantees"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimal cur matrix decompositions"
    - "randomized algorithms for low rank matrix factorizations sharp performance bounds"
    - "low rank approximation and regression in input sparsity time"
    - "simple and deterministic matrix sketching"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"

Connected through hyperedge HE-8 (induced by source paper "distributed low rank approximation of implicit functions of a matrix"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "communication optimal distributed principal component analysis in the column partition model"
    - "distributed kernel principal component analysis"
    - "improved distributed principal component analysis"
    - "fastfood approximate kernel expansions in loglinear time"
    - "principal component analysis and higher correlations for distributed data"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"
    - "an optimal lower bound on the communication complexity of gap hamming distance"

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)