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: "identifying outliers in large matrices via randomized adaptive compressive sampling"
  Abstract: This paper examines the problem of locating outlier columns in a large, otherwise low-rank, matrix. We propose a simple two-step adaptive sensing and inference approach and establish theoretical guarantees for its performance; our results show that accurate outlier identification is achievable using very few linear summaries of the original data matrix—as few as the squared rank of the low-rank component plus the number of outliers, times constant and logarithmic factors. We demonstrate the performance of our approach experimentally in two stylized applications, one motivated by robust collaborative filtering tasks, and the other by saliency map estimation tasks arising in computer vision and automated surveillance, and also investigate extensions to settings where the data are noisy, or possibly incomplete.

Connected through hyperedge HE-1 (induced by source paper "detection of thin boundaries between different types of anomalies in outlier detection using enhanced neural networks"):
  with members:
    - "xgbod improving supervised outlier detection with unsupervised representation learning"
    - "explaining anomalies in groups with characterizing subspace rules"

Connected through hyperedge HE-2 (induced by source paper "a dictionary based generalization of robust pca part ii applications to hyperspectral demixing"):
  with members:
    - "target based hyperspectral demixing via generalized robust pca"
    - "a dictionary based generalization of robust pca"
    - "simultaneously sparse and low rank abundance matrix estimation for hyperspectral image unmixing"
    - "semi blind source separation via sparse representations and online dictionary learning"
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"
    - "compressive principal component pursuit"
    - "low rank matrix recovery from errors and erasures"
    - "stable principal component pursuit"

Connected through hyperedge HE-3 (induced by source paper "a dictionary based generalization of robust pca part i study of theoretical properties"):
  with members:
    - "target based hyperspectral demixing via generalized robust pca"
    - "a dictionary based generalization of robust pca"
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"
    - "compressive principal component pursuit"
    - "low rank matrix recovery from errors and erasures"
    - "stable principal component pursuit"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "an overview of robust subspace recovery"):
  with members:
    - "fast robust and non convex subspace recovery"
    - "static and dynamic robust pca via low rank sparse matrix decomposition a review"
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "estimation of camera locations in highly corrupted scenarios all about that base no shape trouble"
    - "static and dynamic robust pca and matrix completion a review"
    - "being robust in high dimensions can be practical"
    - "a new rank constraint on multi view fundamental matrices and its application to camera location recovery"
    - "a well tempered landscape for non convex robust subspace recovery"

Connected through hyperedge HE-5 (induced by source paper "fast robust and non convex subspace recovery"):
  with members:
    - "a well tempered landscape for non convex robust subspace recovery"
    - "global convergence of a grassmannian gradient descent algorithm for subspace estimation"
    - "input sparsity and hardness for robust subspace approximation"
    - "complete dictionary recovery over the sphere"
    - "robust computation of linear models by convex relaxation"
    - "on iterative hard thresholding methods for high dimensional m estimation"
    - "understanding alternating minimization for matrix completion"
    - "algorithms and hardness for robust subspace recovery"

Connected through hyperedge HE-6 (induced by source paper "scalable and robust community detection with randomized sketching"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "spatial random sampling a structure preserving data sketching tool"
    - "efficient clustering with limited distance information"
    - "community detection via random and adaptive sampling"
    - "community detection thresholds and the weak ramanujan property"
    - "sparse subspace clustering algorithm theory and applications"
    - "clustering partially observed graphs via convex optimization"
    - "robust principal component analysis"

Connected through hyperedge HE-7 (induced by source paper "a well tempered landscape for non convex robust subspace recovery"):
  with members:
    - "fast robust and non convex subspace recovery"
    - "an overview of robust subspace recovery"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "practical sketching algorithms for low rank matrix approximation"
    - "fast algorithms for robust pca via gradient descent"
    - "matrix completion has no spurious local minimum"
    - "simple efficient and neural algorithms for sparse coding"
    - "global convergence of a grassmannian gradient descent algorithm for subspace estimation"

Connected through hyperedge HE-8 (induced by source paper "coherence pursuit fast simple and robust principal component analysis"):
  with members:
    - "fast robust and non convex subspace recovery"
    - "high dimensional low rank plus sparse matrix decomposition"
    - "innovation pursuit a new approach to subspace clustering"
    - "fast algorithms for robust pca via gradient descent"
    - "a subspace learning approach to high dimensional matrix decomposition with efficient information sampling"
    - "dual principal component pursuit"
    - "robust computation of linear models by convex relaxation"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"

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)