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: "a practical guide to randomized matrix computations with matlab implementations"
  Abstract: Matrix operations such as matrix inversion, eigenvalue decomposition, singular value decomposition are ubiquitous in real-world applications. Unfortunately, many of these matrix operations so time and memory expensive that they are prohibitive when the scale of data is large. In real-world applications, since the data themselves are noisy, machine-precision matrix operations are not necessary at all, and one can sacrice a reasonable amount of accuracy for

Connected through hyperedge HE-1 (induced by source paper "weighted gradient coding with leverage score sampling"):
  with members:
    - "numerically stable binary gradient coding"
    - "fundamental limits of approximate gradient coding"
    - "gradient coding based on block designs for mitigating adversarial stragglers"
    - "gradient coding with clustering and multi message communication"
    - "distributed stochastic gradient descent using ldgm codes"
    - "erasurehead distributed gradient descent without delays using approximate gradient coding"
    - "gradient coding from cyclic mds codes and expander graphs"
    - "balanced reed solomon codes"

Connected through hyperedge HE-2 (induced by source paper "oversketched newton fast convex optimization for serverless systems"):
  with members:
    - "cloud programming simplified a berkeley view on serverless computing"
    - "harnessing the power of serverless runtimes for large scale optimization"
    - "serverless computing one step forward two steps back"
    - "a distributed second order algorithm you can trust"
    - "speeding up distributed machine learning using codes"
    - "numpywren serverless linear algebra"
    - "oversketch approximate matrix multiplication for the cloud"
    - "giant globally improved approximate newton method for distributed optimization"

Connected through hyperedge HE-3 (induced by source paper "oversketch approximate matrix multiplication for the cloud"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "numpywren serverless linear algebra"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "occupy the cloud distributed computing for the 99"
    - "serverless computing current trends and open problems"
    - "a sequential approximation framework for coded distributed optimization"
    - "avoiding communication in primal and dual block coordinate descent methods"

Connected through hyperedge HE-4 (induced by source paper "large scale constrained linear regression revisited faster algorithms via preconditioning"):
  with members:
    - "solving ridge regression using sketched preconditioned svrg"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "faster sgd using sketched conditioning"
    - "implementing randomized matrix algorithms in parallel and distributed environments"
    - "randomized sketches of convex programs with sharp guarantees"
    - "mini batch primal and dual methods for svms"
    - "recovering the optimal solution by dual random projection"
    - "near optimal column based matrix reconstruction"

Connected through hyperedge HE-5 (induced by source paper "efficient data driven geologic feature characterization from pre stack seismic measurements using randomized machine learning algorithm"):
  with members:
    - "scalable kernel k means clustering with nystrom approximation relative error bounds"
    - "large scale kernel learning using block coordinate descent"
    - "hierarchically compositional kernels for scalable nonparametric learning"
    - "faster kernel ridge regression using sketching and preconditioning"
    - "towards more efficient spsd matrix approximation and cur matrix decomposition"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "sharp analysis of low rank kernel matrix approximations"
    - "faster least squares approximation"

Connected through hyperedge HE-6 (induced by source paper "single pass pca of large high dimensional data"):
  with members:
    - "sketching as a tool for numerical linear algebra"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"
    - "improved distributed principal component analysis"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "distributed representations of words and phrases and their compositionality"
    - "randomized algorithms for low rank matrix factorizations sharp performance bounds"
    - "simple and deterministic matrix sketching"

Connected through hyperedge HE-7 (induced by source paper "effective sketching methods for value function approximation"):
  with members:
    - "efficient second order online learning by sketching"
    - "accelerated gradient temporal difference learning"
    - "unifying task specification in reinforcement learning"
    - "communication optimal distributed principal component analysis in the column partition model"
    - "optimal principal component analysis in distributed and streaming models"
    - "on convergence of emphatic temporal difference learning"
    - "sketching as a tool for numerical linear algebra"

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.MS(Mathematical Software)