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: "stability over time of modified cs and ls cs for recursive causal sparse reconstruction"
  Abstract: In this work, we obtain sufficient conditions for the ``stability" of our recently proposed algorithms, modified-CS (for noisy measurements) and Least Squares CS-residual (LS-CS), designed for recursive reconstruction of sparse signal sequences from noisy measurements. By ``stability" we mean that the number of misses from the current support estimate and the number of extras in it remain bounded by a time-invariant value at all times. The concept is meaningful only if the bound is small compared to the current signal support size. A direct corollary is that the reconstruction errors are also bounded by a time-invariant and small value.

Connected through hyperedge HE-1 (induced by source paper "support predicted modified cs for recursive robust principal components pursuit"):
  with members:
    - "reprocs a missing link between recursive robust pca and recursive sparse recovery in large but correlated noise"
    - "ls cs residual ls cs compressive sensing on least squares residual"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "modified cs modifying compressive sensing for problems with partially known support"):
  with members:
    - "ls cs residual ls cs compressive sensing on least squares residual"
    - "modified basis pursuit denoising modified bpdn for noisy compressive sensing with partially known support"
    - "weighted ell_1 minimization for sparse recovery with prior information"
    - "analyzing least squares and kalman filtered compressed sensing"
    - "kalman filtered compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "ls cs residual ls cs compressive sensing on least squares residual"):
  with members:
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "kf cs compressive sensing on kalman filtered residual"
    - "analyzing least squares and kalman filtered compressed sensing"
    - "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)