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: "uniform recovery of fusion frame structured sparse signals"
  Abstract: We consider the problem of recovering fusion frame sparse signals from incomplete measurements. These signals are composed of a small number of nonzero blocks taken from a family of subspaces. First, we show that, by using a-priori knowledge of a coherence parameter associated with the angles between the subspaces, one can uniformly recover fusion frame sparse signals with a significantly reduced number of vector-valued (sub-)Gaussian measurements via mixed l^1/l^2-minimization. We prove this by establishing an appropriate version of the restricted isometry property. Our result complements previous nonuniform recovery results in this context, and provides stronger stability guarantees for noisy measurements and approximately sparse signals. Second, we determine the minimal number of scalar-valued measurements needed to uniformly recover all fusion frame sparse signals via mixed l^1/l^2-minimization. This bound is achieved by scalar-valued subgaussian measurements. In particular, our result shows that the number of scalar-valued subgaussian measurements cannot be further reduced using knowledge of the coherence parameter. As a special case it implies that the best known uniform recovery result for block sparse signals using subgaussian measurements is optimal.

Connected through hyperedge HE-1 (induced by source paper "the importance of phase in complex compressive sensing"):
  with members:
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "compressive optical deflectometric tomography a constrained total variation minimization approach"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-2 (induced by source paper "the restricted isometry property of block diagonal matrices for group sparse signal recovery"):
  with members:
    - "gelfand numbers related to structured sparsity and besov space embeddings with small mixed smoothness"
    - "uniform recovery from subgaussian multi sensor measurements"
    - "the restricted isometry property for random block diagonal matrices"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "model based compressive sensing"

Connected through hyperedge HE-3 (induced by source paper "quantized compressive sensing with rip matrices the benefit of dithering"):
  with members:
    - "instance optimal decoding and the restricted isometry property"
    - "fast binary embeddings and quantized compressed sensing with structured matrices"
    - "non gaussian hyperplane tessellations and robust one bit compressed sensing"
    - "quantized compressed sensing for partial random circulant matrices"
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "optimizing quantization for lasso recovery"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"

Connected through hyperedge HE-4 (induced by source paper "sparse recovery of fusion frame structured signals"):
  with members:
    - "sparse recovery from combined fusion frame measurements"
    - "nonuniform sparse recovery with gaussian matrices"

Connected through hyperedge HE-5 (induced by source paper "compressed sensing in hilbert spaces"):
  with members:
    - "sketching for large scale learning of mixture models"
    - "compressive k means"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"

Connected through hyperedge HE-6 (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"
    - "shift invariance sparse coding for audio classification"

Connected through hyperedge HE-7 (induced by source paper "gelfand numbers related to structured sparsity and besov space embeddings with small mixed smoothness"):
  with members:
    - "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "on the absence of the rip in real world applications of compressed sensing and the rip in levels"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "one bit compressed sensing by linear programming"
    - "optimization with sparsity inducing penalties"
    - "block sparse signals uncertainty relations and efficient recovery"

Connected through hyperedge HE-8 (induced by source paper "time for dithering fast and quantized random embeddings via the restricted isometry property"):
  with members:
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "near optimal sample complexity bounds for circulant binary embedding"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "self calibration and biconvex compressive sensing"
    - "isometric sketching of any set via the restricted isometry property"
    - "representation and coding of signal geometry"

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)