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: "ell_p row sampling by lewis weights"
  Abstract: We give a simple algorithm to efficiently sample the rows of a matrix while preserving the p-norms of its product with vectors. Given an $n$-by-$d$ matrix $\boldsymbol{\mathit{A}}$, we find with high probability and in input sparsity time an $\boldsymbol{\mathit{A}}'$ consisting of about $d \log{d}$ rescaled rows of $\boldsymbol{\mathit{A}}$ such that $\| \boldsymbol{\mathit{A}} \boldsymbol{\mathit{x}} \|_1$ is close to $\| \boldsymbol{\mathit{A}}' \boldsymbol{\mathit{x}} \|_1$ for all vectors $\boldsymbol{\mathit{x}}$. We also show similar results for all $\ell_p$ that give nearly optimal sample bounds in input sparsity time. Our results are based on sampling by "Lewis weights", which can be viewed as statistical leverage scores of a reweighted matrix. We also give an elementary proof of the guarantees of this sampling process for $\ell_1$.

Connected through hyperedge HE-1 (induced by source paper "a near optimal algorithm for approximating the john ellipsoid"):
  with members:
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "total least squares regression in input sparsity time"
    - "nearly optimal dynamic k means clustering for high dimensional data"
    - "sensitivity sampling over dynamic geometric data streams with applications to k clustering"
    - "subspace embedding and linear regression with orlicz norm"
    - "relative error tensor low rank approximation"
    - "near optimal sketching of low rank tensor regression"
    - "sketching for kronecker product regression and p splines"

Connected through hyperedge HE-2 (induced by source paper "a practical guide to randomized matrix computations with matlab implementations"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "towards more efficient spsd matrix approximation and cur matrix decomposition"
    - "communication optimal distributed principal component analysis in the column partition model"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "optimal principal component analysis in distributed and streaming models"
    - "sketching as a tool for numerical linear algebra"
    - "the modified nystrom method theories algorithms and extension"

Connected through hyperedge HE-3 (induced by source paper "efficient inverse maintenance and faster algorithms for linear programming"):
  with members:
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "powers of tensors and fast matrix multiplication"
    - "uniform sampling for matrix approximation"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "path finding i solving linear programs with o sqrt rank linear system solves"
    - "path finding ii an o m sqrt n algorithm for the minimum cost flow problem"
    - "low rank approximation and regression in input sparsity time"
    - "iterative row sampling"

Connected through hyperedge HE-4 (induced by source paper "input sparsity and hardness for robust subspace approximation"):
  with members:
    - "relative errors for deterministic low rank matrix approximations"
    - "subspace embeddings and ell_p regression using exponential random variables"
    - "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"

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)