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: "ls cs residual ls cs compressive sensing on least squares residual"
  Abstract: We consider the problem of recursively and causally reconstructing time sequences of sparse signals (with unknown and time-varying sparsity patterns) from a limited number of noisy linear measurements. The sparsity pattern is assumed to change slowly with time. The key idea of our proposed solution, LS-CS-residual (LS-CS), is to replace compressed sensing (CS) on the observation by CS on the least squares (LS) residual computed using the previous estimate of the support. We bound CS-residual error and show that when the number of available measurements is small, the bound is much smaller than that on CS error if the sparsity pattern changes slowly enough. Most importantly, under fairly mild assumptions, we show “stability” of LS-CS over time for a signal model that allows support additions and removals, and that allows coefficients to gradually increase (decrease) until they reach a constant value (become zero). By “stability,” we mean that the number of misses and extras in the support estimate remain bounded by time-invariant values (in turn implying a time-invariant bound on LS-CS error). Numerical experiments, and a dynamic MRI example, backing our claims are shown.

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 "distributed compressive sensing a deep learning approach"):
  with members:
    - "two dimensional pattern coupled sparse bayesian learning via generalized approximate message passing"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "a deep learning approach to structured signal recovery"
    - "learning input and recurrent weight matrices in echo state networks"
    - "sequence transduction with recurrent neural networks"
    - "advances in optimizing recurrent networks"
    - "structured compressed sensing from theory to applications"
    - "model based compressive sensing"

Connected through hyperedge HE-3 (induced by source paper "tracking tensor subspaces with informative random sampling for real time mr imaging"):
  with members:
    - "rank regularization and bayesian inference for tensor completion and extrapolation"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-4 (induced by source paper "real time dynamic mri reconstruction using stacked denoising autoencoder"):
  with members:
    - "marginalized denoising autoencoders for domain adaptation"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "blind compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "adaptive rate compressive sensing using side information"):
  with members:
    - "task driven adaptive statistical compressive sensing of gaussian mixture models"
    - "the stone transform multi resolution image enhancement and real time compressive video"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"
    - "sequential compressed sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "kalman filtered compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "prior support knowledge aided sparse bayesian learning with partly erroneous support information"):
  with members:
    - "dynamic compressive sensing of time varying signals via approximate message passing"
    - "regularized modified bpdn for noisy sparse reconstruction with partial erroneous support and signal value knowledge"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "weighted ell_1 minimization for sparse recovery with prior information"

Connected through hyperedge HE-7 (induced by source paper "time invariant error bounds for modified cs based sparse signal sequence recovery"):
  with members:
    - "on partial sparse recovery"
    - "regularized modified bpdn for noisy sparse reconstruction with partial erroneous support and signal value knowledge"
    - "subspace methods for joint sparse recovery"
    - "exact dynamic support tracking with multiple measurement vectors using compressive music"
    - "support predicted modified cs for recursive robust principal components pursuit"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "sparse signal reconstruction via iterative support detection"
    - "recovering compressively sampled signals using partial support information"

Connected through hyperedge HE-8 (induced by source paper "dynamic compressive sensing of time varying signals via approximate message passing"):
  with members:
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "expectation maximization gaussian mixture approximate message passing"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "online identification and tracking of subspaces from highly incomplete information"
    - "compressive imaging using approximate message passing and a markov tree prior"
    - "generalized approximate message passing for estimation with random linear mixing"

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)