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: "subsampling for ridge regression via regularized volume sampling"
  Abstract: Given $n$ vectors $\mathbf{x}_i\in \mathbb{R}^d$, we want to fit a linear regression model for noisy labels $y_i\in\mathbb{R}$. The ridge estimator is a classical solution to this problem. However, when labels are expensive, we are forced to select only a small subset of vectors $\mathbf{x}_i$ for which we obtain the labels $y_i$. We propose a new procedure for selecting the subset of vectors, such that the ridge estimator obtained from that subset offers strong statistical guarantees in terms of the mean squared prediction error over the entire dataset of $n$ labeled vectors. The number of labels needed is proportional to the statistical dimension of the problem which is often much smaller than $d$. Our method is an extension of a joint subsampling procedure called volume sampling. A second major contribution is that we speed up volume sampling so that it is essentially as efficient as leverage scores, which is the main i.i.d. subsampling procedure for this task. Finally, we show theoretically and experimentally that volume sampling has a clear advantage over any i.i.d. sampling when labels are expensive.

Connected through hyperedge HE-1 (induced by source paper "exact expressions for double descent and implicit regularization via surrogate random design"):
  with members:
    - "understanding overfitting peaks in generalization error analytical risk curves for l 2 and l 1 penalized interpolation"
    - "traditional and heavy tailed self regularization in neural network models"
    - "two models of double descent for weak features"
    - "harmless interpolation of noisy data in regression"
    - "distributed estimation of the inverse hessian by determinantal averaging"
    - "implicit regularization in deep matrix factorization"
    - "bayesian experimental design using regularized determinantal point processes"
    - "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression"

Connected through hyperedge HE-2 (induced by source paper "bayesian experimental design using regularized determinantal point processes"):
  with members:
    - "distributed estimation of the inverse hessian by determinantal averaging"
    - "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression"
    - "correcting the bias in least squares regression with volume rescaled sampling"
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "proportional volume sampling and approximation algorithms for a optimal design"
    - "reverse iterative volume sampling for linear regression"
    - "leveraged volume sampling for linear regression"
    - "lectures on randomized numerical linear algebra"

Connected through hyperedge HE-3 (induced by source paper "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression"):
  with members:
    - "correcting the bias in least squares regression with volume rescaled sampling"
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "proportional volume sampling and approximation algorithms for a optimal design"
    - "reverse iterative volume sampling for linear regression"
    - "leveraged volume sampling for linear regression"
    - "lectures on randomized numerical linear algebra"
    - "unbiased estimates for linear regression via volume sampling"
    - "efficient sampling for k determinantal point processes"

Connected through hyperedge HE-4 (induced by source paper "random quadratic forms with dependence applications to restricted isometry and beyond"):
  with members:
    - "structured linear contextual bandits a sharp and geometric smoothed analysis"
    - "mitigating bias in adaptive data gathering via differential privacy"
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "leveraged volume sampling for linear regression"
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "structured compressed sensing from theory to applications"
    - "randomized algorithms for matrices and data"
    - "a sparse johnson lindenstrauss transform"

Connected through hyperedge HE-5 (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"
    - "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"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-6 (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"
    - "robust and scalable column row sampling from corrupted big data"
    - "video summarization with long short term memory"

Connected through hyperedge HE-7 (induced by source paper "tail bounds for volume sampled linear regression"):
  with members:
    - "fair and diverse dpp based data summarization"
    - "relative error tensor low rank approximation"
    - "unbiased estimates for linear regression via volume sampling"
    - "how to be fair and diverse"
    - "sketching as a tool for numerical linear algebra"
    - "pipage rounding pessimistic estimators and matrix concentration"
    - "randomized algorithms for matrices and data"
    - "note on sampling without replacing from a finite collection of matrices"

Connected through hyperedge HE-8 (induced by source paper "proportional volume sampling and approximation algorithms for a optimal design"):
  with members:
    - "unbiased estimates for linear regression via volume sampling"
    - "real stable polynomials and matroids optimization and counting"
    - "on largest volume simplices and sub determinants"
    - "randomized rounding for the largest simplex problem"
    - "deterministic feature selection for k means clustering"
    - "efficient volume sampling for row column subset selection"

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.LG(Machine Learning)