human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "faster svd truncated least squares regression"):
  Abstract: We develop a fast algorithm for computing the "SVD-truncated" regularized solution to the least-squares problem: $ \min_{\x} \TNorm{\matA \x - \b}. $ Let $\matA_k$ of rank $k$ be the best rank $k$ matrix computed via the SVD of $\matA$. Then, the SVD-truncated regularized solution is: $ \x_k = \pinv{\matA}_k \b. $ If $\matA$ is $m \times n$, then, it takes $O(m n \min\{m,n\})$ time to compute $\x_k $ using the SVD of \math{\matA}. We give an approximation algorithm for \math{\x_k} which constructs a rank-\math{k} approximation $\tilde{\matA}_{k}$ and computes $ \tilde{\x}_{k} = \pinv{\tilde\matA}_{k} \b$ in roughly $O(\nnz(\matA) k \log n)$ time. Our algorithm uses a randomized variant of the subspace iteration. We show that, with high probability: $ \TNorm{\matA \tilde{\x}_{k} - \b} \approx \TNorm{\matA \x_k - \b}$ and $\TNorm{\x_k - \tilde\x_k} \approx 0. $

Includes members:
  Member "spectral clustering via the power method provably":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "smoothed analysis of the condition numbers and growth factors of matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the distance between two neural networks and the stability of learning")

Question: Please tell me which class the source paper 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)