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: "thresholding based efficient outlier robust pca"
  Abstract: We consider the problem of outlier robust PCA (OR-PCA) where the goal is to recover principal directions despite the presence of outlier data points. That is, given a data matrix $M^*$, where $(1-\alpha)$ fraction of the points are noisy samples from a low-dimensional subspace while $\alpha$ fraction of the points can be arbitrary outliers, the goal is to recover the subspace accurately. Existing results for \OR-PCA have serious drawbacks: while some results are quite weak in the presence of noise, other results have runtime quadratic in dimension, rendering them impractical for large scale applications. #R##N#In this work, we provide a novel thresholding based iterative algorithm with per-iteration complexity at most linear in the data size. Moreover, the fraction of outliers, $\alpha$, that our method can handle is tight up to constants while providing nearly optimal computational complexity for a general noise setting. For the special case where the inliers are obtained from a low-dimensional subspace with additive Gaussian noise, we show that a modification of our thresholding based method leads to significant improvement in recovery error (of the subspace) even in the presence of a large fraction of outliers.

Connected through hyperedge HE-1 (induced by source paper "dual principal component pursuit probability analysis and efficient algorithms"):
  with members:
    - "nonconvex robust low rank matrix recovery"
    - "fast robust and non convex subspace recovery"
    - "an overview of robust subspace recovery"
    - "subgradient descent learns orthogonal dictionaries"
    - "a well tempered landscape for non convex robust subspace recovery"
    - "phase transitions of spectral initialization for high dimensional nonconvex estimation"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "provable self representation based outlier detection in a union of subspaces"

Connected through hyperedge HE-2 (induced by source paper "mixture models robustness and sum of squares proofs"):
  with members:
    - "learning geometric concepts with nasty noise"
    - "outlier robust moment estimation via sum of squares"
    - "being robust in high dimensions can be practical"
    - "on the bit complexity of sum of squares proofs"
    - "exact tensor completion with sum of squares"
    - "better agnostic clustering via relaxed tensor norms"
    - "robustly learning a gaussian getting optimal error efficiently"
    - "the power of sum of squares for detecting hidden structures"

Connected through hyperedge HE-3 (induced by source paper "provable self representation based outlier detection in a union of subspaces"):
  with members:
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "dual principal component pursuit"
    - "robust computation of linear models by convex relaxation"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "very deep convolutional networks for large scale image recognition"
    - "greedy feature selection for subspace clustering"

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.LG(Machine Learning)