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: "near oracle performance of greedy block sparse estimation techniques from noisy measurements"
  Abstract: This paper examines the ability of greedy algorithms to estimate a block sparse parameter vector from noisy measurements. In particular, block sparse versions of the orthogonal matching pursuit and thresholding algorithms are analyzed under both adversarial and Gaussian noise models. In the adversarial setting, it is shown that estimation accuracy comes within a constant factor of the noise power. Under Gaussian noise, the Cramer-Rao bound is derived, and it is shown that the greedy techniques come close to this bound at high signal-to-noise ratio. The guarantees are numerically compared with the actual performance of block and non-block algorithms, identifying situations in which block sparse techniques improve upon the scalar sparsity approach. Specifically, we show that block sparse methods are particularly successful when the atoms within each block are nearly orthogonal.

Connected through hyperedge HE-1 (induced by source paper "improved support recovery guarantees for the group lasso with applications to structural health monitoring"):
  with members:
    - "corrupted sensing novel guarantees for separating structured signals"
    - "on block coherence of frames"
    - "the degrees of freedom of the group lasso for a general design"
    - "subspace methods for joint sparse recovery"
    - "measurement bounds for sparse signal ensembles via graphical models"
    - "recovery of block sparse representations from noisy observations via orthogonal matching pursuit"
    - "structured compressed sensing from theory to applications"
    - "sparse recovery from combined fusion frame measurements"

Connected through hyperedge HE-2 (induced by source paper "group sparse recovery via the ell 0 ell 2 penalty theory and algorithm"):
  with members:
    - "a primal dual active set algorithm with continuation for compressed sensing"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "model based compressive sensing"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "stopping condition for greedy block sparse signal recovery"):
  with members:
    - "many access for small packets based on precoding and sparsity aware recovery"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "sparse signal reconstruction via iterative support detection"

Connected through hyperedge HE-4 (induced by source paper "many access for small packets based on precoding and sparsity aware recovery"):
  with members:
    - "capacity of gaussian many access channels"
    - "a novel uplink data transmission scheme for small packets in massive mimo system"
    - "many access channels the gaussian case with random user activities"
    - "scma codebook design"
    - "massive mimo for next generation wireless systems"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "structured compressed sensing from theory to applications"
    - "reduced dimension multiuser detection"

Connected through hyperedge HE-5 (induced by source paper "block sparsity induced adaptive filter for multi clustering system identification"):
  with members:
    - "reweighted l1 norm penalized lms for sparse channel estimation and its analysis"
    - "efficient recovery of block sparse signals via zero point attracting projection"
    - "performance analysis of l_0 norm constraint least mean square algorithm"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "sparse recovery using smoothed ell 0 sl0 convergence analysis"
    - "model based compressive sensing"
    - "l_ 0 norm constraint lms algorithm for sparse system identification"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"

Connected through hyperedge HE-6 (induced by source paper "multichannel group sparsity methods for compressive channel estimation in doubly selective multicarrier mimo systems extended version"):
  with members:
    - "compressed sensing off the grid"
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "model based compressive sensing"
    - "compressive estimation of doubly selective channels in multicarrier systems leakage effects and sparsity enhancing processing"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "capacity of sparse multipath channels in the ultra wideband regime"

Connected through hyperedge HE-7 (induced by source paper "multiple access for small packets based on precoding and sparsity aware detection"):
  with members:
    - "massive mimo for next generation wireless systems"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "asynchronous code division random access using convex optimization"
    - "structured compressed sensing from theory to applications"
    - "energy and spectral efficiency of very large multiuser mimo systems"
    - "model based compressive sensing"

Connected through hyperedge HE-8 (induced by source paper "a novel uplink data transmission scheme for small packets in massive mimo system"):
  with members:
    - "massive mimo for next generation wireless systems"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "structured compressed sensing from theory to applications"
    - "energy and spectral efficiency of very large multiuser mimo systems"
    - "model based compressive 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)