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: "improved practical matrix sketching with guarantees"
  Abstract: Matrices have become essential data representations for many large-scale problems in data analytics, and hence matrix sketching is a critical task. Although much research has focused on improving the error/size tradeoff under various sketching paradigms, the many forms of error bounds make these approaches hard to compare in theory and in practice. This paper attempts to categorize and compare most known methods under row-wise streaming updates with provable guarantees, and then to tweak some of these methods to gain practical improvements while retaining guarantees. #R##N#For instance, we observe that a simple heuristic iSVD, with no guarantees, tends to outperform all known approaches in terms of size/error trade-off. We modify the best performing method with guarantees FrequentDirections under the size/error trade-off to match the performance of iSVD and retain its guarantees. We also demonstrate some adversarial datasets where iSVD performs quite poorly. In comparing techniques in the time/error trade-off, techniques based on hashing or sampling tend to perform better. In this setting we modify the most studied sampling regime to retain error guarantee but obtain dramatic improvements in the time/error trade-off. #R##N#Finally, we provide easy replication of our studies on APT, a new testbed which makes available not only code and datasets, but also a computing platform with fixed environmental settings.

Connected through hyperedge HE-1 (induced by source paper "a deterministic streaming sketch for ridge regression"):
  with members:
    - "robust frequent directions with application in online learning"
    - "efficient frequent directions algorithm for sparse matrices"
    - "online row sampling"
    - "frequent directions simple and deterministic matrix sketching"
    - "low rank approximation and regression in input sparsity time"
    - "simple and deterministic matrix sketching"

Connected through hyperedge HE-2 (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"
    - "frequent directions simple and deterministic matrix sketching"

Connected through hyperedge HE-3 (induced by source paper "moses a streaming algorithm for linear dimensionality reduction"):
  with members:
    - "robust frequent directions with application in online learning"
    - "what to expect when you are expecting on the grassmannian"
    - "fixed rank approximation of a positive semidefinite matrix from streaming data"
    - "streaming principal component analysis from incomplete data"
    - "weighted matrix completion and recovery with prior subspace information"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "learning the differential correlation matrix of a smooth function from point samples"

Connected through hyperedge HE-4 (induced by source paper "robust frequent directions with application in online learning"):
  with members:
    - "introduction to online convex optimization"
    - "efficient second order online learning by sketching"
    - "co occuring directions sketching for approximate matrix multiply"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "sketching as a tool for numerical linear algebra"
    - "provable deterministic leverage score sampling"
    - "spsd matrix approximation vis column selection theories algorithms and extensions"

Connected through hyperedge HE-5 (induced by source paper "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"):
  with members:
    - "randomized single view algorithms for low rank matrix approximation"
    - "group component analysis for multiblock data common and individual feature extraction"
    - "convolutional rectifier networks as generalized tensor decompositions"
    - "tensor networks for latent variable analysis part i algorithms for tensor train decomposition"
    - "linked component analysis from matrices to high order tensors applications to biomedical data"
    - "parallel tensor compression for large scale scientific data"
    - "tensor ring decomposition"
    - "smooth parafac decomposition for tensor completion"

Connected through hyperedge HE-6 (induced by source paper "efficient frequent directions algorithm for sparse matrices"):
  with members:
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimal cur matrix decompositions"
    - "relative errors for deterministic low rank matrix approximations"
    - "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-7 (induced by source paper "co occuring directions sketching for approximate matrix multiply"):
  with members:
    - "frequent directions simple and deterministic matrix sketching"
    - "optimal approximate matrix product in terms of stable rank"
    - "simple and deterministic matrix sketching"

Connected through hyperedge HE-8 (induced by source paper "streaming kernel principal component analysis"):
  with members:
    - "sketching as a tool for numerical linear algebra"
    - "fastfood approximate kernel expansions in loglinear time"
    - "matrix coherence and the nystrom method"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "relative errors for deterministic low rank matrix approximations"
    - "low rank approximation and regression in input sparsity time"
    - "random feature maps for dot product kernels"
    - "simple and deterministic matrix sketching"

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)