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: "sparse recovery from combined fusion frame measurements"
  Abstract: Sparse representations have emerged as a powerful tool in signal and information processing, culminated by the success of new acquisition and processing techniques such as compressed sensing (CS). Fusion frames are very rich new signal representation methods that use collections of subspaces instead of vectors to represent signals. This work combines these exciting fields to introduce a new sparsity model for fusion frames. Signals that are sparse under the new model can be compressively sampled and uniquely reconstructed in ways similar to sparse signals using standard CS. The combination provides a promising new set of mathematical tools and signal models useful in a variety of applications. With the new model, a sparse signal has energy in very few of the subspaces of the fusion frame, although it does not need to be sparse within each of the subspaces it occupies. This sparsity model is captured using a mixed l1/l2 norm for fusion frames. A signal sparse in a fusion frame can be sampled using very few random projections and exactly reconstructed using a convex optimization that minimizes this mixed l1/l2 norm. The provided sampling conditions generalize coherence and RIP conditions used in standard CS theory. It is demonstrated that they are sufficient to guarantee sparse recovery of any signal sparse in our model. More over, a probabilistic analysis is provided using a stochastic model on the sparse signal that shows that under very mild conditions the probability of recovery failure decays exponentially with in creasing dimension of the subspaces.

Connected through hyperedge HE-1 (induced by source paper "sparse recovery of fusion frame structured signals"):
  with members:
    - "uniform recovery of fusion frame structured sparse signals"
    - "nonuniform sparse recovery with gaussian matrices"

Connected through hyperedge HE-2 (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"
    - "near oracle performance of greedy block sparse estimation techniques from noisy measurements"
    - "recovery of block sparse representations from noisy observations via orthogonal matching pursuit"
    - "structured compressed sensing from theory to applications"

Connected through hyperedge HE-3 (induced by source paper "on the global local dichotomy in sparsity modeling"):
  with members:
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "local sparsity and recovery of fusion frames structured signals"
    - "working locally thinking globally part ii stability and algorithms for convolutional sparse coding"
    - "approximation vector machines for large scale online learning"
    - "structured sparsity discrete and convex approaches"
    - "boosting of image denoising algorithms"
    - "sparse modeling for image and vision processing"
    - "uniform recovery of fusion frame structured sparse signals"

Connected through hyperedge HE-4 (induced by source paper "universal spatiotemporal sampling sets for discrete spatially invariant evolution processes"):
  with members:
    - "system identification in dynamical sampling"
    - "exact reconstruction of signals in evolutionary systems via spatiotemporal trade off"
    - "spatially distributed sampling and reconstruction"
    - "discrete sampling and interpolation universal sampling sets for discrete bandlimited spaces"

Connected through hyperedge HE-5 (induced by source paper "compressed sensing and parallel acquisition"):
  with members:
    - "sparsity and parallel acquisition optimal uniform and nonuniform recovery guarantees"
    - "optimal sparse recovery for multi sensor measurements"
    - "wireless compressive sensing over fading channels with distributed sparse random projections"
    - "self calibration and biconvex compressive sensing"
    - "refined analysis of sparse mimo radar"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "multi view in lensless compressive imaging"
    - "wireless compressive sensing for energy harvesting sensor nodes"

Connected through hyperedge HE-6 (induced by source paper "local sparsity and recovery of fusion frames structured signals"):
  with members:
    - "compressed sensing and parallel acquisition"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "uniform recovery of fusion frame structured sparse signals"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "on partial sparse recovery"
    - "compressed sensing with general frames via optimal dual based ell _ 1 analysis"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "average case analysis of multichannel sparse recovery using convex relaxation"

Connected through hyperedge HE-7 (induced by source paper "distributed compressive sensing performance analysis with diverse signal ensembles"):
  with members:
    - "on the exact recovery condition of simultaneous orthogonal matching pursuit"
    - "living on the edge phase transitions in convex programs with random data"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "measurement bounds for sparse signal ensembles via graphical models"
    - "orthogonal matching pursuit with replacement"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"
    - "rank awareness in joint sparse recovery"
    - "average case analysis of multichannel sparse recovery using convex relaxation"

Connected through hyperedge HE-8 (induced by source paper "compressed subspace matching on the continuum"):
  with members:
    - "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "compressive parameter estimation in awgn"
    - "matched filtering from limited frequency samples"
    - "near minimax line spectral estimation"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "blind deconvolution using convex programming"
    - "stable manifold embeddings with structured random matrices"

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)