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: "practical sketching algorithms for low rank matrix approximation"
  Abstract: This paper describes a suite of algorithms for constructing low-rank approximations of an input matrix from a random linear image, or sketch, of the matrix. These methods can preserve structural properties of the input matrix, such as positive-semidefiniteness, and they can produce approximations with a user-specified rank. The algorithms are simple, accurate, numerically stable, and provably correct. Moreover, each method is accompanied by an informative error bound that allows users to select parameters a priori to achieve a given approximation quality. These claims are supported by numerical experiments with real and synthetic data.

Connected through hyperedge HE-1 (induced by source paper "streaming low rank matrix approximation with an application to scientific simulation"):
  with members:
    - "low rank tucker approximation of a tensor from streaming data"
    - "randomized single view algorithms for low rank matrix approximation"
    - "fast and space optimal low rank factorization in the streaming model with application in differential privacy"
    - "parallel tensor compression for large scale scientific data"
    - "dimensionality reduction of massive sparse datasets using coresets"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimal principal component analysis in distributed and streaming models"
    - "improved bounds on sample size for implicit matrix trace estimators"

Connected through hyperedge HE-2 (induced by source paper "alpha alpha rank practically scaling alpha rank through stochastic optimisation"):
  with members:
    - "a generalized training approach for multiagent learning"
    - "multiagent evaluation under incomplete information"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "emergent coordination through competition"
    - "multi agent generalized recursive reasoning"
    - "openspiel a framework for reinforcement learning in games"
    - "bi level actor critic for multi agent coordination"
    - "open ended learning in symmetric zero sum games"

Connected through hyperedge HE-3 (induced by source paper "study of compressed randomized utv decompositions for low rank matrix approximations in data science"):
  with members:
    - "subspace orbit randomized decomposition for low rank matrix approximations"
    - "branch and bound precoding for multiuser mimo systems with 1 bit quantization"
    - "widely linear precoding for large scale mimo with iqi algorithms and performance analysis"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "iterative detection and decoding algorithms for mimo systems in block fading channels using ldpc codes"
    - "robust adaptive beamforming based on low rank and cross correlation techniques"
    - "fast randomized singular value thresholding for low rank optimization"
    - "adaptive reduced rank receive processing based on minimum symbol error rate criterion for large scale multiple antenna systems"

Connected through hyperedge HE-4 (induced by source paper "matrix completion with deterministic pattern a geometric perspective"):
  with members:
    - "on deterministic sampling patterns for robust low rank matrix completion"
    - "fundamental conditions for low cp rank tensor completion"
    - "rank determination for low rank data completion"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "guaranteed matrix completion via non convex factorization"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "incoherence optimal matrix completion"
    - "recovering low rank matrices from few coefficients in any basis"

Connected through hyperedge HE-5 (induced by source paper "fast generalized matrix regression with applications in machine learning"):
  with members:
    - "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"
    - "near optimal column based matrix reconstruction"

Connected through hyperedge HE-6 (induced by source paper "low rank tucker approximation of a tensor from streaming data"):
  with members:
    - "streaming low rank matrix approximation with an application to scientific simulation"
    - "tthresh tensor compression for multidimensional visual data"
    - "a practical randomized cp tensor decomposition"
    - "sketching for kronecker product regression and p splines"
    - "parallel tensor compression for large scale scientific data"
    - "decomposition of big tensors with low multilinear rank"
    - "tensor decompositions a new concept in brain data analysis"
    - "low rank approximation and regression in input sparsity time"

Connected through hyperedge HE-7 (induced by source paper "don t take it lightly phasing optical random projections with unknown operators"):
  with members:
    - "holographic phase retrieval and reference design"
    - "localization from incomplete euclidean distance matrix performance analysis for the svd mds approach"
    - "holographic phase retrieval and optimal reference design"
    - "random projections through multiple optical scattering approximating kernels at the speed of light"
    - "orthogonal random features"
    - "euclidean distance matrices essential theory algorithms and applications"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phase retrieval an overview of recent developments"

Connected through hyperedge HE-8 (induced by source paper "one pass sparsified gaussian mixtures"):
  with members:
    - "streaming low rank matrix approximation with an application to scientific simulation"
    - "optimality of the johnson lindenstrauss lemma"
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "lower bounds for oblivious subspace embeddings"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "sparser johnson lindenstrauss transforms"

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)