human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "efficient l1 norm principal component analysis via bit flipping"):
  Abstract: It was shown recently that the   $K$   L1-norm principal components (L1-PCs) of a real-valued data matrix   $\mathbf X \in \mathbb {R}^{D \times N}$   (  $N$    data samples of   $D$   dimensions) can be exactly calculated with cost   $\mathcal {O}(2^{NK})$    or, when advantageous,   $\mathcal {O}(N^{dK - K + 1})$   where   $d=\mathrm{rank}(\mathbf X)$   ,   $K  . In applications where   $\mathbf X$   is large (e.g., “big” data of large   $N$   and/or “heavy” data of large   $d$  ), these costs are prohibitive. In this paper, we present a novel suboptimal algorithm for the calculation of the   $K    L1-PCs of    $\mathbf X$   of cost    $\mathcal O (ND \mathrm{min} \lbrace N,D\rbrace + N^2K^2(K^2 + d))$  , which is comparable to that of standard L2-norm PC analysis. Our theoretical and experimental studies show that the proposed algorithm calculates the exact optimal L1-PCs with high frequency and achieves higher value in the L1-PC optimization metric than any known alternative algorithm of comparable computational cost. The superiority of the calculated L1-PCs over standard L2-PCs (singular vectors) in characterizing potentially faulty data/measurements is demonstrated with experiments in data dimensionality reduction and disease diagnosis from genomic data.

Includes members:
  Member "optimal algorithms for l_ 1 subspace signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust subspace recovery with adversarial outliers")
  Member "robust principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")

Question: Please tell me which class the source paper 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)