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 "low rank approximation and regression in input sparsity time"):
  Abstract: We design a new distribution over $\poly(r \eps^{-1}) \times n$ matrices $S$ so that for any fixed $n \times d$ matrix $A$ of rank $r$, with probability at least 9/10, $\norm{SAx}_2 = (1 \pm \eps)\norm{Ax}_2$ simultaneously for all $x \in \mathbb{R}^d$. Such a matrix $S$ is called a \emph{subspace embedding}. Furthermore, $SA$ can be computed in $\nnz(A) + \poly(d \eps^{-1})$ time, where $\nnz(A)$ is the number of non-zero entries of $A$. This improves over all previous subspace embeddings, which required at least $\Omega(nd \log d)$ time to achieve this property. We call our matrices $S$ \emph{sparse embedding matrices}. #R##N#Using our sparse embedding matrices, we obtain the fastest known algorithms for $(1+\eps)$-approximation for overconstrained least-squares regression, low-rank approximation, approximating all leverage scores, and $\ell_p$-regression. The leading order term in the time complexity of our algorithms is $O(\nnz(A))$ or $O(\nnz(A)\log n)$. #R##N#We optimize the low-order $\poly(d/\eps)$ terms in our running times (or for rank-$k$ approximation, the $n*\poly(k/eps)$ term), and show various tradeoffs. For instance, we also use our methods to design new preconditioners that improve the dependence on $\eps$ in least squares regression to $\log 1/\eps$. Finally, we provide preliminary experimental results which suggest that our algorithms are competitive in practice.

Includes members:
  Member "sketching as a tool for numerical linear algebra":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "quantile regression for large scale applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient symmetric norm regression via linear sketching")
  Member "subspace embeddings and ell_p regression using exponential random variables":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast fixed dimension l2 subspace embeddings of arbitrary accuracy with application to l1 and l2 tasks")
  Member "lower bounds for oblivious subspace embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "streaming low rank matrix approximation with an application to scientific simulation")
  Member "random projections for linear support vector machines":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the effectiveness of johnson lindenstrauss transform for high dimensional optimization with outliers")
  Member "improved matrix algorithms via the subsampled randomized hadamard transform":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improved subsampled randomized hadamard transform for linear svm")
  Member "sparsity lower bounds for dimensionality reducing maps":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "privacy preserving learning via deep net pruning")
  Member "osnap faster numerical linear algebra algorithms via sparser subspace embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sketching transformed matrices with applications to natural language processing")

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)