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: "kalman filtered compressed sensing"
  Abstract: We consider the problem of reconstructing time sequences of spatially sparse signals (with unknown and time-varying sparsity patterns) from a limited number of linear "incoherent" measurements, in real-time. The signals are sparse in some transform domain referred to as the sparsity basis. For a single spatial signal, the solution is provided by Compressed Sensing (CS). The question that we address is, for a sequence of sparse signals, can we do better than CS, if (a) the sparsity pattern of the signal's transform coefficients' vector changes slowly over time, and (b) a simple prior model on the temporal dynamics of its current non-zero elements is available. The overall idea of our solution is to use CS to estimate the support set of the initial signal's transform vector. At future times, run a reduced order Kalman filter with the currently estimated support and estimate new additions to the support set by applying CS to the Kalman innovations or filtering error (whenever it is "large").

Connected through hyperedge HE-1 (induced by source paper "designing recurrent neural networks by unfolding an l1 l1 minimization algorithm"):
  with members:
    - "maximal sparsity with deep networks"
    - "unsupervised learning of video representations using lstms"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "how to construct deep recurrent neural networks"
    - "time invariant error bounds for modified cs based sparse signal sequence recovery"

Connected through hyperedge HE-2 (induced by source paper "iterated extended kalman smoother based variable splitting for l_1 regularized state estimation"):
  with members:
    - "image fusion with cosparse analysis operator"
    - "dynamic compressive sensing of time varying signals via approximate message passing"
    - "doubly robust smoothing of dynamical processes via outlier sparsity constraints"

Connected through hyperedge HE-3 (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-4 (induced by source paper "compressed sensing for wireless communications useful tips and tricks"):
  with members:
    - "structured compressive sensing based spatio temporal joint channel estimation for fdd massive mimo"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "compressive sensing with prior support quality information and application to massive mimo channel estimation with temporal correlation"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"

Connected through hyperedge HE-5 (induced by source paper "fpa cs focal plane array based compressive imaging in short wave infrared"):
  with members:
    - "lisens a scalable architecture for video compressive sensing"
    - "compressive coded aperture keyed exposure imaging with optical flow reconstruction"
    - "coded aperture compressive temporal imaging"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-6 (induced by source paper "design and analysis of a greedy pursuit for distributed compressed sensing"):
  with members:
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "greedy sparsity promoting algorithms for distributed learning"
    - "analysis of democratic voting principles used in distributed greedy algorithms"
    - "distributed compressed sensing for static and time varying networks"
    - "distributed greedy pursuit algorithms"
    - "cooperative sparsity pattern recovery in distributed networks via distributed omp"
    - "typical l 1 recovery limit of sparse vectors represented by concatenations of random orthogonal matrices"
    - "projection based and look ahead strategies for atom selection"

Connected through hyperedge HE-7 (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"
    - "ls cs residual ls cs compressive sensing on least squares residual"
    - "sequential compressed sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-8 (induced by source paper "statistical recovery of simultaneously sparse time varying signals from multiple measurement vectors"):
  with members:
    - "dynamic compressive sensing of time varying signals via approximate message passing"
    - "generalized orthogonal matching pursuit"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "subspace pursuit for compressive sensing signal reconstruction"

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)