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: "low rank methods in event detection"
  Abstract: We present low-rank methods for event detection. We assume that normal observation come from a low-rank subspace, prior to being corrupted by a uniformly distributed noise. Correspondingly, we aim at recovering a representation of the subspace, and perform event detection by running point-to-subspace distance query in $\ell^\infty$, for each incoming observation. In particular, we use a variant of matrix completion under interval uncertainty on a suitable flattening $M \in R^{m \times n}$ of the input data to obtain a low-rank model $M \approx L \times R$, $L \in R^{m \times r}$, $R \in R^{r \times n}$, $r \ll m$. On-line, we compute the distance of each incoming $x \in R^n$ to the space spanned by $R$. For the distance computation, we present a constant-time algorithm with a one-sided error bounded by a function of the number of coordinates employed.

Connected through hyperedge HE-1 (induced by source paper "clipped matrix completion a remedy for ceiling effects"):
  with members:
    - "sub sampled cubic regularization for non convex optimization"
    - "the algebraic combinatorial approach for low rank matrix completion"
    - "recovering low rank matrices from few coefficients in any basis"
    - "matrix completion from noisy entries"
    - "a simpler approach to matrix completion"
    - "matrix completion with noise"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

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.DS(Data Structures and Algorithms)