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: "subspace embeddings and ell_p regression using exponential random variables"
  Abstract: Oblivious low-distortion subspace embeddings are a crucial building block for numerical linear algebra problems. We show for any real $p, 1 \leq p < \infty$, given a matrix $M \in \mathbb{R}^{n \times d}$ with $n \gg d$, with constant probability we can choose a matrix $\Pi$ with $\max(1, n^{1-2/p}) \poly(d)$ rows and $n$ columns so that simultaneously for all $x \in \mathbb{R}^d$, $\|Mx\|_p \leq \|\Pi Mx\|_{\infty} \leq \poly(d) \|Mx\|_p.$ Importantly, $\Pi M$ can be computed in the optimal $O(\nnz(M))$ time, where $\nnz(M)$ is the number of non-zero entries of $M$. This generalizes all previous oblivious subspace embeddings which required $p \in [1,2]$ due to their use of $p$-stable random variables. Using our matrices $\Pi$, we also improve the best known distortion of oblivious subspace embeddings of $\ell_1$ into $\ell_1$ with $\tilde{O}(d)$ target dimension in $O(\nnz(M))$ time from $\tilde{O}(d^3)$ to $\tilde{O}(d^2)$, which can further be improved to $\tilde{O}(d^{3/2}) \log^{1/2} n$ if $d = \Omega(\log n)$, answering a question of Meng and Mahoney (STOC, 2013). #R##N#We apply our results to $\ell_p$-regression, obtaining a $(1+\eps)$-approximation in $O(\nnz(M)\log n) + \poly(d/\eps)$ time, improving the best known $\poly(d/\eps)$ factors for every $p \in [1, \infty) \setminus \{2\}$. If one is just interested in a $\poly(d)$ rather than a $(1+\eps)$-approximation to $\ell_p$-regression, a corollary of our results is that for all $p \in [1, \infty)$ we can solve the $\ell_p$-regression problem without using general convex programming, that is, since our subspace embeds into $\ell_{\infty}$ it suffices to solve a linear programming problem. Finally, we give the first protocols for the distributed $\ell_p$-regression problem for every $p \geq 1$ which are nearly optimal in communication and computation.

Connected through hyperedge HE-1 (induced by source paper "fast fixed dimension l2 subspace embeddings of arbitrary accuracy with application to l1 and l2 tasks"):
  with members:
    - "fast and rip optimal transforms"
    - "quantile regression for large scale applications"
    - "low rank approximation and regression in input sparsity time"
    - "iterative row sampling"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "the fast cauchy transform and faster robust linear regression"
    - "fast approximation of matrix coherence and statistical leverage"
    - "row sampling for matrix algorithms via a non commutative bernstein bound"

Connected through hyperedge HE-2 (induced by source paper "on coresets for logistic regression"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "recursive sampling for the nystr om method"
    - "input sparsity time low rank approximation via ridge leverage score sampling"
    - "k means for streaming and distributed big sparse data"
    - "input sparsity and hardness for robust subspace approximation"
    - "sketching as a tool for numerical linear algebra"
    - "uniform sampling for matrix approximation"
    - "low rank approximation and regression in input sparsity time"

Connected through hyperedge HE-3 (induced by source paper "low rank approximation with entrywise ell_1 norm error"):
  with members:
    - "sub exponential approximation schemes for csps from dense to almost sparse"
    - "optimal cur matrix decompositions"
    - "relative errors for deterministic low rank matrix approximations"
    - "iterative row sampling"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "computing a nonnegative matrix factorization provably"
    - "efficient volume sampling for row column subset selection"

Connected through hyperedge HE-4 (induced by source paper "implementing randomized matrix algorithms in parallel and distributed environments"):
  with members:
    - "sketching as a tool for numerical linear algebra"
    - "quantile regression for large scale applications"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "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"
    - "minimizing communication in linear algebra"

Connected through hyperedge HE-5 (induced by source paper "input sparsity and hardness for robust subspace approximation"):
  with members:
    - "ell_p row sampling by lewis weights"
    - "relative errors for deterministic low rank matrix approximations"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "on the sensitivity of shape fitting problems"
    - "low rank approximation and regression in input sparsity time"
    - "simple and deterministic matrix sketching"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"

Connected through hyperedge HE-6 (induced by source paper "nearly optimal bounds for sparse recovery in generic norms with applications to k median sketching"):
  with members:
    - "improved approximation algorithms for earth mover distance in data streams"
    - "a unified framework for linear dimensionality reduction in l1"
    - "lower bounds for sparse recovery"

Connected through hyperedge HE-7 (induced by source paper "sketching as a tool for numerical linear algebra"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "optimal approximate matrix product in terms of stable rank"
    - "optimal cur matrix decompositions"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "uniform sampling for matrix approximation"
    - "single pass spectral sparsification in dynamic streams"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "lower bounds for oblivious subspace embeddings"

Connected through hyperedge HE-8 (induced by source paper "toward a unified theory of sparse dimensionality reduction in euclidean space"):
  with members:
    - "randomized sketches of convex programs with sharp guarantees"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "uniform recovery of fusion frame structured sparse signals"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "on model based rip 1 matrices"
    - "matching the universal barrier without paying the costs solving linear programs with o sqrt rank linear system solves"

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)