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: "dense error correction via l1 minimization"
  Abstract: This paper studies the problem of recovering a non-negative sparse signal $\x \in \Re^n$ from highly corrupted linear measurements $\y = A\x + \e \in \Re^m$, where $\e$ is an unknown error vector whose nonzero entries may be unbounded. Motivated by an observation from face recognition in computer vision, this paper proves that for highly correlated (and possibly overcomplete) dictionaries $A$, any non-negative, sufficiently sparse signal $\x$ can be recovered by solving an $\ell^1$-minimization problem: $\min \|\x\|_1 + \|\e\|_1 \quad {subject to} \quad \y = A\x + \e.$ More precisely, if the fraction $\rho$ of errors is bounded away from one and the support of $\x$ grows sublinearly in the dimension $m$ of the observation, then as $m$ goes to infinity, the above $\ell^1$-minimization succeeds for all signals $\x$ and almost all sign-and-support patterns of $\e$. This result suggests that accurate recovery of sparse signals is possible and computationally feasible even with nearly 100% of the observations corrupted. The proof relies on a careful characterization of the faces of a convex polytope spanned together by the standard crosspolytope and a set of iid Gaussian vectors with nonzero mean and small variance, which we call the ``cross-and-bouquet'' model. Simulations and experimental results corroborate the findings, and suggest extensions to the result.

Connected through hyperedge HE-1 (induced by source paper "robust pca with partial subspace knowledge"):
  with members:
    - "a correctness result for online robust pca"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "prost a smoothed lp norm robust online subspace tracking method for realtime background subtraction in video"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"
    - "compressive principal component pursuit"
    - "principal component pursuit with reduced linear measurements"
    - "recovering low rank matrices from few coefficients in any basis"

Connected through hyperedge HE-2 (induced by source paper "an online algorithm for separating sparse and low dimensional signal sequences from their sum"):
  with members:
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"
    - "recursive sparse recovery in large but structured noise part 2"
    - "estimation of simultaneously sparse and low rank matrices"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"
    - "sharp recovery bounds for convex demixing with applications"
    - "compressive principal component pursuit"
    - "principal component pursuit with reduced linear measurements"

Connected through hyperedge HE-3 (induced by source paper "recursive sparse recovery in large but structured noise part 2"):
  with members:
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"
    - "estimation of simultaneously sparse and low rank matrices"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"
    - "sharp recovery bounds for convex demixing with applications"
    - "compressive principal component pursuit"
    - "principal component pursuit with reduced linear measurements"
    - "robust pca via outlier pursuit"
    - "real time robust principal components pursuit"

Connected through hyperedge HE-4 (induced by source paper "a framework to characterize performance of lasso algorithms"):
  with members:
    - "a rigorous geometry probability equivalence in characterization of ell_1 optimization"
    - "upper bounding ell_1 optimization weak thresholds"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "model based compressive sensing"
    - "various thresholds for ell_1 optimization in compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "algorithmic linear dimension reduction in the l_1 norm for sparse vectors"

Connected through hyperedge HE-5 (induced by source paper "robust logistic regression using shift parameters long version"):
  with members:
    - "how to grade a test without knowing the answers a bayesian graphical model for adaptive crowdsourcing and aptitude testing"
    - "robust principal component analysis"
    - "introduction to the conll 2003 shared task language independent named entity recognition"
    - "identifying mislabeled training data"

Connected through hyperedge HE-6 (induced by source paper "real time robust principal components pursuit"):
  with members:
    - "dense error correction for low rank matrices via principal component pursuit"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "modified basis pursuit denoising modified bpdn for noisy compressive sensing with partially known support"
    - "robust principal component analysis"

Connected through hyperedge HE-7 (induced by source paper "block length dependent thresholds in block sparse compressed sensing"):
  with members:
    - "block sparse signals uncertainty relations and efficient recovery"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "model based compressive sensing"
    - "weighted ell_1 minimization for sparse recovery with prior information"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "various thresholds for ell_1 optimization in compressed sensing"
    - "sparse recovery of positive signals with minimal expansion"
    - "simultaneously sparse solutions to linear inverse problems with multiple system matrices and a single observation vector"

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.IT(Information Theory)