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: "implementing randomized matrix algorithms in parallel and distributed environments"
  Abstract: In this era of large-scale data, distributed systems built on top of clusters of commodity hardware provide cheap and reliable storage and scalable processing of massive data. Here, we review recent work on developing and implementing randomized matrix algorithms in large-scale parallel and distributed environments. Randomized algorithms for matrix problems have received a great deal of attention in recent years, thus far typically either in theory or in machine learning applications or with implementations on a single machine. Our main focus is on the underlying theory and practical implementation of random projection and random sampling algorithms for very large very overdetermined (i.e., overconstrained) $\ell_1$ and $\ell_2$ regression problems. Randomization can be used in one of two related ways: either to construct sub-sampled problems that can be solved, exactly or approximately, with traditional numerical methods; or to construct preconditioned versions of the original full problem that are easier to solve with traditional iterative algorithms. Theoretical results demonstrate that in near input-sparsity time and with only a few passes through the data one can obtain very strong relative-error approximate solutions, with high probability. Empirical results highlight the importance of various trade-offs (e.g., between the time to construct an embedding and the conditioning quality of the embedding, between the relative importance of computation versus communication, etc.) and demonstrate that $\ell_1$ and $\ell_2$ regression problems can be solved to low, medium, or high precision in existing distributed systems on up to terabyte-sized data.

Connected through hyperedge HE-1 (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"
    - "a practical guide to randomized matrix computations with matlab implementations"
    - "faster sgd using sketched conditioning"
    - "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-2 (induced by source paper "gpu accelerated sub sampled newton s method"):
  with members:
    - "adam a method for stochastic optimization"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "data completion and stochastic algorithms for pde inversion problems with many measurements"
    - "adadelta an adaptive learning rate method"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-3 (induced by source paper "low rank approximation and decomposition of large matrices using error correcting codes"):
  with members:
    - "fast estimation of approximate matrix ranks using spectral densities"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "restricted isometry property of random subdictionaries"
    - "sketching as a tool for numerical linear algebra"
    - "low rank matrix approximation in linear time"
    - "low rank approximation and regression in input sparsity time"
    - "on almost disjunct matrices for group testing"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"

Connected through hyperedge HE-4 (induced by source paper "faster kernel ridge regression using sketching and preconditioning"):
  with members:
    - "sharper bounds for regression and low rank approximation with regularization"
    - "large scale kernel learning using block coordinate descent"
    - "hierarchically compositional kernels for scalable nonparametric learning"
    - "optimal approximate matrix product in terms of stable rank"
    - "sketching as a tool for numerical linear algebra"
    - "scalable kernel methods via doubly stochastic gradients"
    - "preconditioned krylov solvers for kernel regression"
    - "revisiting the nystrom method for improved large scale machine learning"

Connected through hyperedge HE-5 (induced by source paper "sharper bounds for regression and low rank approximation with regularization"):
  with members:
    - "faster kernel ridge regression using sketching and preconditioning"
    - "optimal approximate matrix product in terms of stable rank"
    - "randomized sketches of convex programs with sharp guarantees"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "low rank approximation and regression in input sparsity time"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "randomized algorithms for matrices and data"
    - "faster least squares approximation"

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.DC(Distributed, Parallel, and Cluster Computing)