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: "lectures on randomized numerical linear algebra"
  Abstract: This chapter is based on lectures on Randomized Numerical Linear Algebra from the 2016 Park City Mathematics Institute summer school on The Mathematics of Data.

Connected through hyperedge HE-1 (induced by source paper "approximating spectral clustering via sampling a review"):
  with members:
    - "optimized deformed laplacian for spectrum based community detection in sparse heterogeneous graphs"
    - "revisiting the bethe hessian improved community detection in sparse heterogeneous graphs"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "graph reduction by local variation"
    - "scalable spectral clustering using random binning features"
    - "graph reduction with spectral and cut guarantees"
    - "spectrally approximating large graphs with smaller graphs"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"

Connected through hyperedge HE-2 (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-3 (induced by source paper "pyhessian neural networks through the lens of the hessian"):
  with members:
    - "hawq v2 hessian aware trace weighted quantization of neural networks"
    - "q bert hessian based ultra low precision quantization of bert"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "inefficiency of k fac for large batch size training"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "large batch size training of neural networks with adversarial training and second order information"

Connected through hyperedge HE-4 (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"
    - "guarantees for greedy maximization of non submodular functions with applications"

Connected through hyperedge HE-5 (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"
    - "unbiased estimates for linear regression via volume sampling"
    - "subsampling for ridge regression via regularized volume sampling"
    - "efficient sampling for k determinantal point processes"

Connected through hyperedge HE-6 (induced by source paper "graph resistance and learning from pairwise comparisons"):
  with members:
    - "maximum selection and ranking under noisy comparisons"
    - "worst case vs average case design for estimation from fixed pairwise comparisons"
    - "sorting and selection with imprecise comparisons"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "concentration of the kirchhoff index for erdős renyi graphs"
    - "sorting from noisy information"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"

Connected through hyperedge HE-7 (induced by source paper "improving classification performance by feature space transformations and model selection"):
  with members:
    - "scalable kernel k means clustering with nystrom approximation relative error bounds"
    - "recursive sampling for the nystr om method"

Connected through hyperedge HE-8 (induced by source paper "numerical linear algebra in the sliding window model"):
  with members:
    - "nearly optimal distinct elements and heavy hitters on sliding windows"
    - "submodular optimization over sliding windows"
    - "sketches for matrix norms faster smaller and more general"
    - "submodular maximization over sliding windows"
    - "online row sampling"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "frequent directions simple and deterministic matrix sketching"
    - "sparsified cholesky solvers for sdd linear systems"

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)