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: "a random algorithm for low rank decomposition of large scale matrices with missing entries"
  Abstract: A random submatrix method (RSM) is proposed to calculate the low-rank decomposition    ${\hat {\text{U}}}_{m\times r}{\hat {\text{V}}}_{n\times r}^{T}$    (   $r  ) of the matrix    $ {\text{Y}}\in R^{m\times n}$    (assuming    $m>n$    generally) with known entry percentage    $0  . RSM is very fast as only    $\mathcal {O}(mr^{2^{^{^{}\!}}}\rho ^{r})$    or    $\mathcal {O}(n^{3}\rho ^{3r})$    floating-point operations (flops) are required, compared favorably with    $\mathcal {O}(mnr+r^{2}(m+n))$    flops required by the state-of-the-art algorithms. Meanwhile, RSM has the advantage of a small memory requirement as only    $\max (n^{2},mr+nr)$    real values need to be saved. With the assumption that known entries are uniformly distributed in    $ {Y}$   , submatrices formed by known entries are randomly selected from    $ {Y}$    with statistical size    $k\times n\rho ^{k}$    or    $m\rho ^{l}\times l$   , where    $k$    or    $l$    takes    $r+1$    usually. We propose and prove a theorem, under random noises the probability that the subspace associated with a smaller singular value will turn into the space associated to anyone of the    $r$    largest singular values is smaller. Based on the theorem, the    $n\rho ^{k}-k$    null vectors or the    $l-r$    right singular vectors associated with the minor singular values are calculated for each submatrix. The vectors ought to be the null vectors of the submatrix formed by the chosen    $n\rho ^{k}$    or    $l$    columns of the ground truth of    ${\hat { {V}}}^{T}$   . If enough submatrices are randomly chosen,    $\hat {V}$    and    $\hat {U}$    can be estimated accordingly. The experimental results on random synthetic matrices with sizes such as    $13\,1072 \times 1024$    and on real data sets such as dinosaur indicate that RSM is    $4.30\sim 197.95$    times faster than the state-of-the-art algorithms. It, meanwhile, has considerable high precision achieving or approximating to the best.

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)