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: "analyzing least squares and kalman filtered compressed sensing"
  Abstract: In recent work, we studied the problem of causally reconstructing time sequences of spatially sparse signals, with unknown and slow time-varying sparsity patterns, from a limited number of linear “incoherent” measurements. We proposed a solution called Kalman Filtered Compressed Sensing (KF-CS). The key idea is to run a reduced order KF only for the current signal's estimated nonzero coefficients' set, while performing CS on the Kalman filtering error to estimate new additions, if any, to the set. KF may be replaced by Least Squares (LS) estimation and we call the resulting algorithm LS-CS. In this work, (a) we bound the error in performing CS on the LS error and (b) we obtain the conditions under which the KF-CS (or LS-CS) estimate converges to that of a genie-aided KF (or LS), i.e. the KF (or LS) which knows the true nonzero sets.

Connected through hyperedge HE-1 (induced by source paper "recursive recovery of sparse signal sequences from compressive measurements a review"):
  with members:
    - "video compressive sensing for spatial multiplexing cameras using motion flow models"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "fpa cs focal plane array based compressive imaging in short wave infrared"
    - "quadratic basis pursuit"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "sparse recovery of streaming signals using l1 homotopy"
    - "lensless imaging by compressive sensing"

Connected through hyperedge HE-2 (induced by source paper "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"):
  with members:
    - "reconciling compressive sampling systems for spectrally sparse continuous time signals"
    - "regime change bit depth versus measurement rate in compressive sensing"
    - "compressive sensing of analog signals using discrete prolate spheroidal sequences"
    - "noise folding in compressed sensing"
    - "how well can we estimate a sparse vector"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "information theoretic bounds for compressed sensing"

Connected through hyperedge HE-3 (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"
    - "stability over time of modified cs and ls cs for recursive causal sparse reconstruction"
    - "modified basis pursuit denoising modified bpdn for noisy compressive sensing with partially known support"
    - "weighted ell_1 minimization for sparse recovery with prior information"
    - "kalman filtered compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "ls cs residual ls cs compressive sensing on least squares residual"):
  with members:
    - "stability over time of modified cs and ls cs for recursive causal sparse reconstruction"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "kf cs compressive sensing on kalman filtered residual"
    - "kalman filtered compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "stability over time of modified cs and ls cs for recursive causal sparse reconstruction"):
  with members:
    - "ls cs residual ls cs compressive sensing on least squares residual"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "weighted ell_1 minimization for sparse recovery with prior information"
    - "kalman filtered compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "modified basis pursuit denoising modified bpdn for noisy compressive sensing with partially known support"):
  with members:
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "a short note on compressed sensing with partially known signal support"
    - "weighted ell_1 minimization for sparse recovery with prior information"

Connected through hyperedge HE-7 (induced by source paper "kf cs compressive sensing on kalman filtered residual"):
  with members:
    - "ls cs residual ls cs compressive sensing on least squares residual"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "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)