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: "prost a smoothed lp norm robust online subspace tracking method for realtime background subtraction in video"
  Abstract: An increasing number of methods for background subtraction use Robust PCA to identify sparse foreground objects. While many algorithms use the '1-norm as a con- vex relaxation of the ideal sparsifying function, we approach the problem with a smoothed'p-norm and present pROST, a method for robust online subspace tracking. The algorithm is based on alternating minimization on manifolds. Imple- mented on a graphics processing unit it achieves realtime performance. Experimental results on a state-of-the-art bench- mark for background subtraction on real-world video data indicate that the method succeeds at a broad variety of back- ground subtraction scenarios, and it outperforms competing approaches when video quality is deteriorated by camera jit-

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 "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-3 (induced by source paper "network volume anomaly detection and identification in large scale networks based on online time structured traffic tensor tracking"):
  with members:
    - "online low rank tensor subspace tracking from incomplete data by cp decomposition using recursive least squares"
    - "network traffic decomposition for anomaly detection"
    - "estimating traffic and anomaly maps via network tomography"
    - "rank regularization and bayesian inference for tensor completion and extrapolation"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"
    - "structural analysis of network traffic matrix via relaxed principal component pursuit"
    - "online identification and tracking of subspaces from highly incomplete information"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (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-5 (induced by source paper "robust pca with partial subspace knowledge"):
  with members:
    - "a correctness result for online robust pca"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "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"

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.CV(Computer Vision and Pattern Recognition)