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: "completing low rank matrices with corrupted samples from few coefficients in general basis"
  Abstract: Subspace recovery from the corrupted and missing data is crucial for various applications in signal processing and information theory. To complete missing values and detect column corruptions, the existing robust matrix completion (MC) methods mostly concentrate on recovering a low-rank matrix from a few corrupted coefficients with respect to standard basis, which, however, does not apply to more general basis, e.g., Fourier basis. In this paper, we prove that the range space of an    $m\times n$    matrix with rank    $r$    can be exactly recovered from a few coefficients with respect to general basis, though    $r$    and the number of corrupted samples are both as high as    $O(\min \{m,n\}/\log ^{3} (m+n))$   . Our model covers the previous ones as special cases, and robust MC can recover the intrinsic matrix with a higher rank. Moreover, we suggest a universal choice of the regularization parameter, which is    $\lambda =1/\sqrt {\log n}$   . By our    $\ell _{2,1}$    filtering algorithm, which has theoretical guarantees, we can further reduce the computational cost of our model. As an application, we also find that the solutions to extended robust low-rank representation and to our extended robust MC are mutually expressible, so both our theory and algorithm can be applied to the subspace clustering problem with missing values under certain conditions. The experiments verify our theories.

Connected through hyperedge HE-1 (induced by source paper "noise stable rigid graphs for euclidean embedding"):
  with members:
    - "exact reconstruction of euclidean distance geometry problem using low rank matrix completion"
    - "recovering low rank matrices from few coefficients in any basis"

Connected through hyperedge HE-2 (induced by source paper "thresholding based efficient outlier robust pca"):
  with members:
    - "fast algorithms for robust pca via gradient descent"
    - "robust pca in high dimension a deterministic approach"
    - "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)