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: "dynamic updating for ell_ 1 minimization"
  Abstract: The theory of compressive sensing (CS) has shown us that under certain conditions, a sparse signal can be recovered from a small number of linear incoherent measurements. An effective class of reconstruction algorithms involve solving a convex optimization program that balances the l1 norm of the solution against a data fidelity term. Tremendous progress has been made in recent years on algorithms for solving these l1 minimization programs. These algorithms, however, are for the most part static: they focus on finding the solution for a fixed set of measurements. In this paper, we present a suite of dynamic algorithms for solving l1 minimization programs for streaming sets of measurements. We consider cases where the underlying signal changes slightly between measurements, and where new measurements of a fixed signal are sequentially added to the system. We develop algorithms to quickly update the solution of several different types of l1 optimization problems whenever these changes occur, thus avoiding having to solve a new optimization problem from scratch. Our proposed schemes are based on homotopy continuation, which breaks down the solution update in a systematic and efficient way into a small number of linear steps. Each step consists of a low-rank update and a small number of matrix-vector multiplications - very much like recursive least squares. Our investigation also includes dynamic updating schemes for l1 decoding problems, where an arbitrary signal is to be recovered from redundant coded measurements which have been corrupted by sparse errors.

Connected through hyperedge HE-1 (induced by source paper "semi supervised sparse representation based classification for face recognition with insufficient labeled samples"):
  with members:
    - "symmetric non rigid structure from motion for category specific object structure estimation"
    - "exploiting symmetry and or manhattan properties for 3d object structure estimation from single and multiple images"
    - "sparse illumination learning and transfer for single sample face recognition with image corruption and misalignment"
    - "constructing a non negative low rank and sparse graph with data adaptive features"
    - "deeply learned face representations are sparse selective and robust"

Connected through hyperedge HE-2 (induced by source paper "a survey of sparse representation algorithms and applications"):
  with members:
    - "group based sparse representation for image restoration"
    - "l_ 1 2 regularization convergence of iterative half thresholding algorithm"
    - "constructing a non negative low rank and sparse graph with data adaptive features"
    - "sparse recovery of streaming signals using l1 homotopy"
    - "compressed sensing signal recovery via forward backward pursuit"
    - "an mdl framework for sparse coding and dictionary learning"
    - "image super resolution via dual dictionary learning and sparse representation"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"

Connected through hyperedge HE-3 (induced by source paper "sparsity aware learning and compressed sensing an overview"):
  with members:
    - "experiments with random projection"
    - "compressive principal component pursuit"
    - "stable image reconstruction using total variation minimization"
    - "recovering low rank matrices from few coefficients in any basis"
    - "online sparse system identification and signal reconstruction using projections onto weighted ell_ 1 balls"
    - "structured compressed sensing from theory to applications"
    - "performance analysis of sparse recovery based on constrained minimal singular values"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"

Connected through hyperedge HE-4 (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"
    - "ls cs residual ls cs compressive sensing on least squares residual"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "sparse signal reconstruction via iterative support detection"

Connected through hyperedge HE-5 (induced by source paper "compressive link acquisition in multiuser communications"):
  with members:
    - "asynchronous code division random access using convex optimization"
    - "gps signal acquisition via compressive multichannel sampling"
    - "reduced dimension multiuser detection"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "compressed sensing of analog signals in shift invariant spaces"
    - "on off random access channels a compressed sensing framework"

Connected through hyperedge HE-6 (induced by source paper "proppa a fast algorithm for ell_1 minimization and low rank matrix completion"):
  with members:
    - "online identification and tracking of subspaces from highly incomplete information"
    - "guaranteed rank minimization via singular value projection"

Connected through hyperedge HE-7 (induced by source paper "exact recoverability from dense corrupted observations via l_1 minimization"):
  with members:
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "a probabilistic and ripless theory of compressed sensing"
    - "stable principal component pursuit"
    - "robust pca via outlier pursuit"
    - "dense error correction for low rank matrices via principal component pursuit"
    - "matrix completion with noise"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "collaborative spectrum sensing from sparse observations in cognitive radio networks"):
  with members:
    - "collaborative spectrum sensing from sparse observations using matrix completion for cognitive radio networks"
    - "matrix completion from a few entries"
    - "coalitional games for distributed collaborative spectrum sensing in cognitive radio networks"
    - "cooperative spectrum sensing using random matrix theory"

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)