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: "efficient volume sampling for row column subset selection"
  Abstract: We give efficient algorithms for volume sampling, i.e., for picking $k$-subsets of the rows of any given matrix with probabilities proportional to the squared volumes of the simplices defined by them and the origin (or the squared volumes of the parallelepipeds defined by these subsets of rows). This solves an open problem from the monograph on spectral algorithms by Kannan and Vempala. Our first algorithm for volume sampling $k$-subsets of rows from an $m$-by-$n$ matrix runs in $O(kmn^{\omega} \log n)$ arithmetic operations and a second variant of it for $(1+\epsilon)$-approximate volume sampling runs in $O(mn \log m \cdot k^{2}/\epsilon^{2} + m \log^{\omega} m \cdot k^{2\omega+1}/\epsilon^{2\omega} \cdot \log(k \epsilon^{-1} \log m))$ arithmetic operations, which is almost linear in the size of the input (i.e., the number of entries) for small $k$. Our efficient volume sampling algorithms imply several interesting results for low-rank matrix approximation.

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 "diversity and inclusion metrics for subset selection"):
  with members:
    - "assessing and remedying coverage for a given dataset"
    - "the price of fair pca one extra dimension"
    - "ranking with fairness constraints"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "measuring fairness in ranked outputs"
    - "how to be fair and diverse"
    - "learning mixtures of submodular shells with application to document summarization"
    - "fairness through awareness"

Connected through hyperedge HE-3 (induced by source paper "adversarially robust low dimensional representations"):
  with members:
    - "vc classes are adversarially robustly learnable but only improperly"
    - "how hard is robust mean estimation"
    - "optimal average case reductions to sparse pca from weak assumptions to strong hardness"
    - "on robustness to adversarial examples and polynomial optimization"
    - "approximating operator norms via generalized krivine rounding"
    - "a spectral view of adversarially robust features"
    - "high dimensional robust mean estimation in nearly linear time"
    - "high accuracy low precision training"

Connected through hyperedge HE-4 (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"
    - "optimal column based low rank matrix reconstruction"
    - "near optimal column based matrix reconstruction"
    - "compressed matrix multiplication"

Connected through hyperedge HE-5 (induced by source paper "optimal analysis of subset selection based l_p low rank approximation"):
  with members:
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "towards a zero one law for entrywise low rank approximation"
    - "relative error tensor low rank approximation"
    - "how to escape saddle points efficiently"
    - "fast algorithms for robust pca via gradient descent"
    - "low rank approximation of binary matrices column subset selection and generalizations"
    - "optimal cur matrix decompositions"
    - "near optimal column based matrix reconstruction"

Connected through hyperedge HE-6 (induced by source paper "a universal sampling method for reconstructing signals with simple fourier transforms"):
  with members:
    - "relating leverage scores and density using regularized christoffel functions"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "active regression via linear sample sparsification"
    - "fourier sparse interpolation without a frequency gap"
    - "a robust sparse fourier transform in the continuous setting"
    - "principal component projection without principal component analysis"
    - "recursive sampling for the nystr om method"

Connected through hyperedge HE-7 (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"
    - "optimal column based low rank matrix reconstruction"

Connected through hyperedge HE-8 (induced by source paper "iterative projection and matching finding structure preserving representatives and its application to computer vision"):
  with members:
    - "cr gan learning complete representations for multi view generation"
    - "proportional volume sampling and approximation algorithms for a optimal design"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "the kinetics human action video dataset"
    - "deep bayesian active learning with image data"
    - "subsampling for ridge regression via regularized volume sampling"
    - "robust and scalable column row sampling from corrupted big data"

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)