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: "faster least squares approximation"
  Abstract: Least squares approximation is a technique to find an approximate solution to a system of linear equations that has no exact solution. In a typical setting, one lets $n$ be the number of constraints and $d$ be the number of variables, with $n \gg d$. Then, existing exact methods find a solution vector in $O(nd^2)$ time. We present two randomized algorithms that provide very accurate relative-error approximations to the optimal value and the solution vector of a least squares approximation problem more rapidly than existing exact algorithms. Both of our algorithms preprocess the data with the Randomized Hadamard Transform. One then uniformly randomly samples constraints and solves the smaller problem on those constraints, and the other performs a sparse random projection and solves the smaller problem on those projected coordinates. In both cases, solving the smaller problem provides relative-error approximations, and, if $n$ is sufficiently larger than $d$, the approximate solution can be computed in $O(nd \log d)$ time.

Connected through hyperedge HE-1 (induced by source paper "randomized spectral clustering in large scale stochastic block models"):
  with members:
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "accurate community detection in the stochastic block model via spectral algorithms"
    - "randomized algorithms for low rank matrix factorizations sharp performance bounds"
    - "fast approximation of matrix coherence and statistical leverage"
    - "randomized algorithms for matrices and data"
    - "graph sparsification by effective resistances"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "distributed sketching methods for privacy preserving regression"):
  with members:
    - "occupy the cloud distributed computing for the 99"
    - "emnist an extension of mnist to handwritten letters"
    - "randomized sketches of convex programs with sharp guarantees"
    - "sketching as a tool for numerical linear algebra"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-3 (induced by source paper "blinkml efficient maximum likelihood estimation with probabilistic guarantees"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "verdictdb universalizing approximate query processing"
    - "measuring the effects of data parallelism on neural network training"
    - "hemingway modeling distributed optimization algorithms"
    - "unbiased estimates for linear regression via volume sampling"
    - "database learning toward a database that becomes smarter every time"
    - "noscope optimizing neural network queries over video at scale"
    - "clipper a low latency online prediction serving system"

Connected through hyperedge HE-4 (induced by source paper "continual learning via online leverage score sampling"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "on first order meta learning algorithms"
    - "neural processes"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "continual learning with deep generative replay"
    - "continual learning through synaptic intelligence"
    - "gradient episodic memory for continual learning"

Connected through hyperedge HE-5 (induced by source paper "oblivious sketching of high degree polynomial kernels"):
  with members:
    - "almost optimal tensor sketch"
    - "a universal sampling method for reconstructing signals with simple fourier transforms"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "faster kernel ridge regression using sketching and preconditioning"
    - "recursive sampling for the nystr om method"
    - "optimal approximate matrix product in terms of stable rank"
    - "randomized sketches of convex programs with sharp guarantees"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-6 (induced by source paper "total least squares regression in input sparsity time"):
  with members:
    - "tight bounds for the subspace sketch problem with applications"
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "optimal sketching for kronecker product regression and low rank approximation"
    - "a near optimal algorithm for approximating the john ellipsoid"
    - "dimensionality reduction for tukey regression"
    - "solving linear programs in the current matrix multiplication time"
    - "subspace embedding and linear regression with orlicz norm"
    - "relative error tensor low rank approximation"

Connected through hyperedge HE-7 (induced by source paper "fast generalized matrix regression with applications in machine learning"):
  with members:
    - "practical sketching algorithms for low rank matrix approximation"
    - "towards more efficient spsd matrix approximation and cur matrix decomposition"
    - "spsd matrix approximation vis column selection theories algorithms and extensions"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "fast approximation of matrix coherence and statistical leverage"

Connected through hyperedge HE-8 (induced by source paper "matrix sketching for secure collaborative machine learning"):
  with members:
    - "gradient descent with compressed iterates"
    - "exploiting unintended feature leakage in collaborative learning"
    - "differentially private data generative models"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "the johnson lindenstrauss transform itself preserves differential privacy"
    - "fast approximation of matrix coherence and statistical leverage"
    - "randomized algorithms for matrices and 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)