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: "sharper bounds for regression and low rank approximation with regularization"
  Abstract: The technique of matrix sketching, such as the use of random projections, has been shown in recent years to be a powerful tool for accelerating many important statistical learning techniques. Research has so far focused largely on using sketching for the "vanilla" un-regularized versions of these techniques. #R##N#Here we study sketching methods for regularized variants of linear regression, low rank approximations, and canonical correlation analysis. We study regularization both in a fairly broad setting, and in the specific context of the popular and widely used technique of ridge regularization; for the latter, as applied to each of these problems, we show algorithmic resource bounds in which the {\em statistical dimension} appears in places where in previous bounds the rank would appear. The statistical dimension is always smaller than the rank, and decreases as the amount of regularization increases. In particular, for the ridge low-rank approximation problem $\min_{Y,X} \lVert YX - A \rVert_F^2 + \lambda \lVert Y\rVert_F^2 + \lambda\lVert X \rVert_F^2$, where $Y\in\mathbb{R}^{n\times k}$ and $X\in\mathbb{R}^{k\times d}$, we give an approximation algorithm needing \[ O(\mathtt{nnz}(A)) + \tilde{O}((n+d)\varepsilon^{-1}k \min\{k, \varepsilon^{-1}\mathtt{sd}_\lambda(Y^*)\})+ \tilde{O}(\varepsilon^{-8} \mathtt{sd}_\lambda(Y^*)^3) \] time, where $s_{\lambda}(Y^*)\le k$ is the statistical dimension of $Y^*$, $Y^*$ is an optimal $Y$, $\varepsilon$ is an error parameter, and $\mathtt{nnz}(A)$ is the number of nonzero entries of $A$. #R##N#We also study regularization in a much more general setting. For example, we obtain sketching-based algorithms for the low-rank approximation problem $\min_{X,Y} \lVert YX - A \rVert_F^2 + f(Y,X)$ where $f(\cdot,\cdot)$ is a regularizing function satisfying some very general conditions (chiefly, invariance under orthogonal transformations).

Connected through hyperedge HE-1 (induced by source paper "provably robust blackbox optimization for reinforcement learning"):
  with members:
    - "variance reduction for evolution strategies via structured control variates"
    - "introduction to online convex optimization"
    - "policies modulating trajectory generators"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "ppo cma proximal policy optimization with covariance matrix adaptation"
    - "proximal policy optimization algorithms"
    - "orthogonal random features"

Connected through hyperedge HE-2 (induced by source paper "when random search is not enough sample efficient and noise robust blackbox optimization of rl policies"):
  with members:
    - "variance reduction for evolution strategies via structured control variates"
    - "introduction to online convex optimization"
    - "policies modulating trajectory generators"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "ppo cma proximal policy optimization with covariance matrix adaptation"
    - "proximal policy optimization algorithms"
    - "orthogonal random features"

Connected through hyperedge HE-3 (induced by source paper "sublinear time low rank approximation of positive semidefinite matrices"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "practical sketching algorithms for low rank matrix approximation"
    - "randomized single view algorithms for low rank matrix approximation"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "recursive sampling for the nystr om method"
    - "input sparsity time low rank approximation via ridge leverage score sampling"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-4 (induced by source paper "sketching for kronecker product regression and p splines"):
  with members:
    - "relative error tensor low rank approximation"
    - "low rank approximation with entrywise ell_1 norm error"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "the fast cauchy transform and faster robust linear regression"
    - "compressed matrix multiplication"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-5 (induced by source paper "faster kernel ridge regression using sketching and preconditioning"):
  with members:
    - "large scale kernel learning using block coordinate descent"
    - "hierarchically compositional kernels for scalable nonparametric learning"
    - "optimal approximate matrix product in terms of stable rank"
    - "implementing randomized matrix algorithms in parallel and distributed environments"
    - "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"

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)