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: "randomized algorithms for low rank matrix factorizations sharp performance bounds"
  Abstract: The development of randomized algorithms for numerical linear algebra, e.g. for computing approximate QR and SVD factorizations, has recently become an intense area of research. This paper studies one of the most frequently discussed algorithms in the literature for dimensionality reduction---specifically for approximating an input matrix with a low-rank element. We introduce a novel and rather intuitive analysis of the algorithm in Martinsson et al. (2008), which allows us to derive sharp estimates and give new insights about its performance. This analysis yields theoretical guarantees about the approximation error and at the same time, ultimate limits of performance (lower bounds) showing that our upper bounds are tight. Numerical experiments complement our study and show the tightness of our predictions compared with empirical observations.

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"
    - "fast approximation of matrix coherence and statistical leverage"
    - "randomized algorithms for matrices and data"
    - "graph sparsification by effective resistances"
    - "a tutorial on spectral clustering"
    - "faster least squares approximation"

Connected through hyperedge HE-2 (induced by source paper "fast interpolation based t sne for improved visualization of single cell rna seq data"):
  with members:
    - "clustering with t sne provably"

Connected through hyperedge HE-3 (induced by source paper "fast spectral ranking for similarity search"):
  with members:
    - "unifying local and non local signal processing with graph cnns"
    - "geometric deep learning going beyond euclidean data"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"
    - "fast exact and multi scale inference for semantic image segmentation with deep gaussian crfs"

Connected through hyperedge HE-4 (induced by source paper "single pass pca of large high dimensional data"):
  with members:
    - "a practical guide to randomized matrix computations with matlab implementations"
    - "sketching as a tool for numerical linear algebra"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"
    - "improved distributed principal component analysis"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "distributed representations of words and phrases and their compositionality"
    - "simple and deterministic matrix sketching"

Connected through hyperedge HE-5 (induced by source paper "compressive pca for low rank matrices on graphs"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "stationary signal processing on graphs"
    - "fast robust pca on graphs"
    - "network topology identification from spectral templates"
    - "compressive spectral clustering"
    - "fast randomized singular value thresholding for nuclear norm minimization"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "robust principal component analysis on graphs"

Connected through hyperedge HE-6 (induced by source paper "fast robust pca on graphs"):
  with members:
    - "connections between nuclear norm and frobenius norm based representations"
    - "stationary signal processing on graphs"
    - "fast randomized singular value thresholding for nuclear norm minimization"
    - "robust principal component analysis on graphs"
    - "shape interaction matrix revisited and robustified efficient subspace clustering with corrupted and incomplete data"
    - "gspbox a toolbox for signal processing on graphs"
    - "unlocbox a matlab convex optimization toolbox using proximal splitting methods"
    - "matrix completion on graphs"

Connected through hyperedge HE-7 (induced by source paper "efficient frequent directions algorithm for sparse matrices"):
  with members:
    - "improved practical matrix sketching with guarantees"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimal cur matrix decompositions"
    - "relative errors for deterministic low rank matrix approximations"
    - "low rank approximation and regression in input sparsity time"
    - "simple and deterministic matrix sketching"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"

Connected through hyperedge HE-8 (induced by source paper "compressive pca on graphs"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "stationary signal processing on graphs"
    - "fast randomized singular value thresholding for nuclear norm minimization"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "robust principal component analysis on graphs"
    - "random sampling of bandlimited signals on graphs"
    - "gspbox a toolbox for signal processing on graphs"
    - "unlocbox a matlab convex optimization toolbox using proximal splitting methods"

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.NA(Numerical Analysis)