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: "parallel implementation of fast randomized algorithms for low rank matrix decomposition"
  Abstract: We analyze the parallel performance of randomized interpolative decomposition by decomposing low rank complex-valued Gaussian random matrices of about 100 GB. We chose a Cray XMT supercomputer as it provides an almost ideal PRAM model permitting quick investigation of parallel algorithms without obfuscation from hardware idiosyncrasies. We obtain that on non-square matrices performance scales almost linearly with runtime about 100 times faster on 128 processors. We also verify that numerically discovered error bounds still hold on matrices two orders of magnitude larger than those previously tested.

Connected through hyperedge HE-1 (induced by source paper "deriving compact laws based on algebraic formulation of a data set"):
  with members:
    - "a preliminary review of influential works in data driven discovery"
    - "julia a fresh approach to numerical computing"

Connected through hyperedge HE-2 (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-3 (induced by source paper "robust principal component analysis on graphs"):
  with members:
    - "on the optimality of shape and data representation in the spectral domain"
    - "randomized algorithms for low rank matrix factorizations sharp performance bounds"
    - "stable principal component pursuit"
    - "robust pca via outlier pursuit"
    - "robust principal component analysis"

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.DC(Distributed, Parallel, and Cluster Computing)