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: "reprocs a missing link between recursive robust pca and recursive sparse recovery in large but correlated noise"
  Abstract: This work studies the recursive robust principal components' analysis (PCA) problem. Here, "robust" refers to robustness to both independent and correlated sparse outliers, although we focus on the latter. A key application where this problem occurs is in video surveillance where the goal is to separate a slowly changing background from moving foreground objects on-the-fly. The background sequence is well modeled as lying in a low dimensional subspace, that can gradually change over time, while the moving foreground objects constitute the correlated sparse outliers. In this and many other applications, the foreground is an outlier for PCA but is actually the "signal of interest" for the application; where as the background is the corruption or noise. Thus our problem can also be interpreted as one of recursively recovering a time sequence of sparse signals in the presence of large but spatially correlated noise. #R##N#This work has two key contributions. First, we provide a new way of looking at this problem and show how a key part of our solution strategy involves solving a noisy compressive sensing (CS) problem. Second, we show how we can utilize the correlation of the outliers to our advantage in order to even deal with very large support sized outliers. The main idea is as follows. The correlation model applied to the previous support estimate helps predict the current support. This prediction serves as "partial support knowledge" for solving the modified-CS problem instead of CS. The support estimate of the modified-CS reconstruction is, in turn, used to update the correlation model parameters using a Kalman filter (or any adaptive filter). We call the resulting approach "support-predicted modified-CS".

Connected through hyperedge HE-1 (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-2 (induced by source paper "an online algorithm for separating sparse and low dimensional signal sequences from their sum"):
  with members:
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"
    - "recursive sparse recovery in large but structured noise part 2"
    - "estimation of simultaneously sparse and low rank matrices"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"
    - "sharp recovery bounds for convex demixing with applications"
    - "compressive principal component pursuit"
    - "principal component pursuit with reduced linear measurements"

Connected through hyperedge HE-3 (induced by source paper "exact reconstruction conditions for regularized modified basis pursuit"):
  with members:
    - "regularized modified bpdn for noisy sparse reconstruction with partial erroneous support and signal value knowledge"
    - "compressive imaging using approximate message passing and a markov tree prior"
    - "ls cs residual ls cs compressive sensing on least squares residual"
    - "sparse signal reconstruction via iterative support detection"
    - "model based compressive sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "kalman filtered compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "regularized modified bpdn for noisy sparse reconstruction with partial erroneous support and signal value knowledge"):
  with members:
    - "compressive imaging using approximate message passing and a markov tree prior"
    - "ls cs residual ls cs compressive sensing on least squares residual"
    - "model based compressive sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "modified basis pursuit denoising modified bpdn for noisy compressive sensing with partially known support"
    - "kalman filtered compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "support predicted modified cs for recursive robust principal components pursuit"):
  with members:
    - "ls cs residual ls cs compressive sensing on least squares residual"
    - "stability over time of modified cs and ls cs for recursive causal sparse reconstruction"
    - "dense error correction for low rank matrices via principal component pursuit"
    - "real time robust principal components pursuit"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "robust principal component analysis"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "kalman filtered compressed sensing"

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)