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 correctness result for online robust pca"
  Abstract: This work studies the problem of sequentially recovering a sparse vector $x_t$ and a vector from a low-dimensional subspace $l_t$ from knowledge of their sum $m_t = x_t + l_t$. If the primary goal is to recover the low-dimensional subspace where the $l_t$'s lie, then the problem is one of online or recursive robust principal components analysis (PCA). To the best of our knowledge, this is the first correctness result for online robust PCA. We prove that if the $l_t$'s obey certain denseness and slow subspace change assumptions, and the support of $x_t$ changes by at least a certain amount at least every so often, and some other mild assumptions hold, then with high probability, the support of $x_t$ will be recovered exactly, and the error made in estimating $x_t$ and $l_t$ will be small. An example of where such a problem might arise is in separating a sparse foreground and slowly changing dense background in a surveillance video.

Connected through hyperedge HE-1 (induced by source paper "online and offline robust pca novel algorithms and performance guarantees"):
  with members:
    - "online matrix completion and online robust pca"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "robust pca with partial subspace knowledge"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"
    - "stable principal component pursuit"
    - "robust principal component analysis"

Connected through hyperedge HE-2 (induced by source paper "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "can dmd obtain a scene background in color"
    - "efficient background modeling based on sparse representation and outlier iterative removal"
    - "fast algorithms for robust pca via gradient descent"
    - "fast robust pca on graphs"
    - "approximated robust principal component analysis for improved general scene background subtraction"
    - "mpi faun an mpi based framework for alternating updating nonnegative matrix factorization"

Connected through hyperedge HE-3 (induced by source paper "online matrix completion and online robust pca"):
  with members:
    - "performance guarantees for reprocs correlated low rank matrix entries case"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "robust pca with partial subspace knowledge"
    - "local convergence of an algorithm for subspace identification from partial data"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"
    - "low rank matrix recovery from errors and erasures"
    - "online identification and tracking of subspaces from highly incomplete information"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "robust pca with partial subspace knowledge"):
  with members:
    - "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"
    - "low rank matrix recovery from errors and erasures"

Connected through hyperedge HE-5 (induced by source paper "performance guarantees for reprocs correlated low rank matrix entries case"):
  with members:
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"
    - "real time robust principal components 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.IT(Information Theory)