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: "sparsity averaging for compressive imaging"
  Abstract: We discuss a novel sparsity prior for compressive imaging in the context of the theory of compressed sensing with coherent redundant dictionaries, based on the observation that natural images exhibit strong average sparsity over multiple coherent frames. We test our prior and the associated algorithm, based on an analysis reweighted   $\ell _{1}$   formulation, through extensive numerical simulations on natural images for spread spectrum and random Gaussian acquisition schemes. Our results show that average sparsity outperforms state-of-the-art priors that promote sparsity in a single orthonormal basis or redundant frame, or that promote gradient sparsity. Code and test data are available at  https://github.com/basp-group/sopt .

Connected through hyperedge HE-1 (induced by source paper "sparse signal recovery via generalized entropy functions minimization"):
  with members:
    - "sparse signal recovery using generalized approximate message passing with built in parameter estimation"
    - "oblique pursuits for compressed sensing"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing"
    - "robust principal component analysis"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "sparse image reconstruction on the sphere analysis and synthesis"):
  with members:
    - "localisation of directional scale discretised wavelets on the sphere"
    - "second generation curvelets on the sphere"
    - "sampling sparse signals on the sphere algorithms and applications"
    - "accurate reconstruction of finite rate of innovation signals on the sphere"
    - "gauss legendre sampling on the rotation group"
    - "consistent discretization and minimization of the l1 norm on manifolds"
    - "directional spin wavelets on the sphere"
    - "a novel sampling theorem on the rotation group"

Connected through hyperedge HE-3 (induced by source paper "on collaborative compressive sensing systems the framework design and algorithm"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "performance limits for noisy multimeasurement vector problems"
    - "cloud k svd a collaborative dictionary learning algorithm for big distributed data"
    - "improving m sbl for joint sparse recovery using a subspace penalty"
    - "approximating sampled sinusoids and multiband signals using multiband modulated dpss dictionaries"
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "sparse signal reconstruction via iterative support detection"
    - "distributed compressive sensing"

Connected through hyperedge HE-4 (induced by source paper "modulated unit norm tight frames for compressed sensing"):
  with members:
    - "convolutional compressed sensing using deterministic sequences"
    - "projection design for statistical compressive sensing a tight frame based approach"
    - "the restricted isometry property for random block diagonal matrices"
    - "spread spectrum magnetic resonance imaging"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques"
    - "a probabilistic and ripless theory of compressed sensing"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-5 (induced by source paper "iteratively reweighted ell_1 approaches to sparse composite regularization"):
  with members:
    - "sparse modeling for image and vision processing"
    - "generalized approximate message passing for cosparse analysis compressive sensing"
    - "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques"

Connected through hyperedge HE-6 (induced by source paper "reverberant audio source separation via sparse and low rank modeling"):
  with members:
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-7 (induced by source paper "generalized approximate message passing for cosparse analysis compressive sensing"):
  with members:
    - "compressive phase retrieval via generalized approximate message passing"
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "variational free energies for compressed sensing"
    - "sparse estimation with the swept approximated message passing algorithm"
    - "on convergence of approximate message passing"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "fast and efficient compressive sensing using structurally random matrices"

Connected through hyperedge HE-8 (induced by source paper "on sparsity averaging"):
  with members:
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques"

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)