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: "optimal column based low rank matrix reconstruction"
  Abstract: We prove that for any real-valued matrix $X \in \R^{m \times n}$, and positive integers $r \ge k$, there is a subset of $r$ columns of $X$ such that projecting $X$ onto their span gives a $\sqrt{\frac{r+1}{r-k+1}}$-approximation to best rank-$k$ approximation of $X$ in Frobenius norm. We show that the trade-off we achieve between the number of columns and the approximation ratio is optimal up to lower order terms. Furthermore, there is a deterministic algorithm to find such a subset of columns that runs in $O(r n m^{\omega} \log m)$ arithmetic operations where $\omega$ is the exponent of matrix multiplication. We also give a faster randomized algorithm that runs in $O(r n m^2)$ arithmetic operations.

Connected through hyperedge HE-1 (induced by source paper "improved guarantees and a multiple descent curve for the column subset selection problem and the nystr om method"):
  with members:
    - "two models of double descent for weak features"
    - "exact sampling of determinantal point processes with sublinear time preprocessing"
    - "bayesian experimental design using regularized determinantal point processes"
    - "exact expressions for double descent and implicit regularization via surrogate random design"
    - "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression"
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "reverse iterative volume sampling for linear regression"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"

Connected through hyperedge HE-2 (induced by source paper "progressive stochastic greedy sparse reconstruction and support selection"):
  with members:
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "lazier than lazy greedy"
    - "greedy column subset selection for large scale data sets"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "near optimal column based matrix reconstruction"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-3 (induced by source paper "fast randomized matrix and tensor interpolative decomposition using countsketch"):
  with members:
    - "a practical randomized cp tensor decomposition"
    - "sketching for kronecker product regression and p splines"
    - "fast randomized algorithms for t product based tensor operations and decompositions with applications to imaging data"
    - "optimal cur matrix decompositions"
    - "low rank approximation and regression in input sparsity time"
    - "near optimal column based matrix reconstruction"
    - "compressed matrix multiplication"
    - "cur from a sparse optimization viewpoint"

Connected through hyperedge HE-4 (induced by source paper "random sampling for distributed coded matrix multiplication"):
  with members:
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "optimal approximate matrix product in terms of stable rank"
    - "optimal cur matrix decompositions"
    - "low rank approximation and regression in input sparsity time"
    - "near optimal column based matrix reconstruction"

Connected through hyperedge HE-5 (induced by source paper "low rank approximation in the presence of outliers"):
  with members:
    - "better agnostic clustering via relaxed tensor norms"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "mixture models robustness and sum of squares proofs"
    - "resilience a criterion for learning in the presence of arbitrary outliers"
    - "learning from untrusted data"
    - "fast algorithms for robust pca via gradient descent"
    - "agnostic estimation of mean and covariance"

Connected through hyperedge HE-6 (induced by source paper "reverse iterative volume sampling for linear regression"):
  with members:
    - "active regression via linear sample sparsification"
    - "unbiased estimates for linear regression via volume sampling"
    - "subsampling for ridge regression via regularized volume sampling"
    - "near optimal coresets for least squares regression"
    - "low rank approximation and regression in input sparsity time"
    - "faster subset selection for matrices and applications"
    - "fast approximation of matrix coherence and statistical leverage"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-7 (induced by source paper "regularized greedy column subset selection"):
  with members:
    - "greedy column subset selection new bounds and distributed algorithms"
    - "unsupervised feature selection with adaptive structure learning"
    - "near optimal column based matrix reconstruction"
    - "efficient volume sampling for row column subset selection"
    - "an improved approximation algorithm for the column subset selection problem"

Connected through hyperedge HE-8 (induced by source paper "correcting the bias in least squares regression with volume rescaled sampling"):
  with members:
    - "fair and diverse dpp based data summarization"
    - "reverse iterative volume sampling for linear regression"
    - "leveraged volume sampling for linear regression"
    - "how to be fair and diverse"

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)