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: "dynamic anomalography tracking network anomalies via sparsity and low rank"
  Abstract: In the backbone of large-scale networks, origin-to-destination (OD) traffic flows experience abrupt unusual changes known as traffic volume anomalies, which can result in congestion and limit the extent to which end-user quality of service requirements are met. As a means of maintaining seamless end-user experience in dynamic environments, as well as for ensuring network security, this paper deals with a crucial network monitoring task termed dynamic anomalography. Given link traffic measurements (noisy superpositions of unobserved OD flows) periodically acquired by backbone routers, the goal is to construct an estimated map of anomalies in real time, and thus summarize the network `health state' along both the flow and time dimensions. Leveraging the low intrinsic-dimensionality of OD flows and the sparse nature of anomalies, a novel online estimator is proposed based on an exponentially-weighted least-squares criterion regularized with the sparsity-promoting l1-norm of the anomalies, and the nuclear norm of the nominal traffic matrix. After recasting the non-separable nuclear norm into a form amenable to online optimization, a real-time algorithm for dynamic anomalography is developed and its convergence established under simplifying technical assumptions. For operational conditions where computational complexity reductions are at a premium, a lightweight stochastic gradient algorithm based on Nesterov's acceleration technique is developed as well. Comprehensive numerical tests with both synthetic and real network data corroborate the effectiveness of the proposed online algorithms and their tracking capabilities, and demonstrate that they outperform state-of-the-art approaches developed to diagnose traffic anomalies.

Connected through hyperedge HE-1 (induced by source paper "static and dynamic robust pca and matrix completion a review"):
  with members:
    - "provable dynamic robust pca or robust subspace tracking"
    - "an overview of robust subspace recovery"
    - "video denoising via dynamic video layering"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "low rank phase retrieval"
    - "nearly optimal robust subspace tracking"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "fast algorithms for robust pca via gradient descent"

Connected through hyperedge HE-2 (induced by source paper "static and dynamic robust pca via low rank sparse matrix decomposition a review"):
  with members:
    - "provable dynamic robust pca or robust subspace tracking"
    - "video denoising via dynamic video layering"
    - "low rank phase retrieval"
    - "nearly optimal robust subspace tracking"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "fast algorithms for robust pca via gradient descent"
    - "correlated pca principal components analysis when data and noise are correlated"
    - "matrix completion has no spurious local minimum"

Connected through hyperedge HE-3 (induced by source paper "video denoising via dynamic video layering"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "fast algorithms for robust pca via gradient descent"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"
    - "stable principal component pursuit"
    - "real time robust principal components pursuit"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "successive convex approximation algorithms for sparse signal estimation with nonconvex regularizations"):
  with members:
    - "a parallel best response algorithm with exact line search for nonconvex sparsity regularized rank minimization"
    - "multidimensional sparse recovery for mimo channel parameter estimation"
    - "decomposition by partial linearization parallel optimization of multi agent systems"
    - "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-5 (induced by source paper "subspace network deep multi task censored regression for modeling neurodegenerative diseases"):
  with members:
    - "deep forest towards an alternative to deep neural networks"
    - "deep residual learning for image recognition"
    - "deepfont identify your font from an image"

Connected through hyperedge HE-6 (induced by source paper "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"):
  with members:
    - "provable dynamic robust pca or robust subspace tracking"
    - "an overview of robust subspace recovery"
    - "low rank phase retrieval"
    - "nearly optimal robust subspace tracking"
    - "robust online matrix factorization for dynamic background subtraction"
    - "fast algorithms for robust pca via gradient descent"
    - "correlated pca principal components analysis when data and noise are correlated"
    - "nearly optimal robust matrix completion"

Connected through hyperedge HE-7 (induced by source paper "consistent tomography under partial observations over adaptive networks"):
  with members:
    - "signal processing on graphs causal modeling of un structured data"
    - "graph reconstruction from the observation of diffused signals"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "distributed detection over adaptive networks refined asymptotics and the role of connectivity"
    - "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime"
    - "signals on graphs uncertainty principle and sampling"
    - "information diffusion of topic propagation in social media"

Connected through hyperedge HE-8 (induced by source paper "fast online low rank tensor subspace tracking by cp decomposition using recursive least squares from incomplete observations"):
  with members:
    - "inductive framework for multi aspect streaming tensor completion with side information"
    - "online low rank tensor subspace tracking from incomplete data by cp decomposition using recursive least squares"
    - "network volume anomaly detection and identification in large scale networks based on online time structured traffic tensor tracking"
    - "low rank tensor completion a riemannian manifold preconditioning approach"
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "res regularized stochastic bfgs algorithm"
    - "rank regularization and bayesian inference for tensor completion and extrapolation"

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.NI(Networking and Internet Architecture)