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: "fast and efficient compressive sensing using structurally random matrices"
  Abstract: This paper introduces a new framework to construct fast and efficient sensing matrices for practical compressive sensing, called Structurally Random Matrix (SRM). In the proposed framework, we prerandomize the sensing signal by scrambling its sample locations or flipping its sample signs and then fast-transform the randomized samples and finally, subsample the resulting transform coefficients to obtain the final sensing measurements. SRM is highly relevant for large-scale, real-time compressive sensing applications as it has fast computation and supports block-based processing. In addition, we can show that SRM has theoretical sensing performance comparable to that of completely random sensing matrices. Numerical simulation results verify the validity of the theory and illustrate the promising potentials of the proposed sensing framework.

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"
    - "sparsity averaging for compressive imaging"
    - "oblique pursuits for compressed sensing"
    - "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 "one pass sparsified gaussian mixtures"):
  with members:
    - "streaming low rank matrix approximation with an application to scientific simulation"
    - "practical sketching algorithms for low rank matrix approximation"
    - "optimality of the johnson lindenstrauss lemma"
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "lower bounds for oblivious subspace embeddings"
    - "sparser johnson lindenstrauss transforms"

Connected through hyperedge HE-3 (induced by source paper "signal recovery under cumulative coherence"):
  with members:
    - "sparse recovery with coherent tight frames via analysis dantzig selector and analysis lasso"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "compressed sensing matrices from fourier matrices"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "compressed sensing and affine rank minimization under restricted isometry"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "sparse approximation property and stable recovery of sparse signals from noisy measurements"
    - "new bounds for restricted isometry constants"

Connected through hyperedge HE-4 (induced by source paper "image compression based on compressive sensing end to end comparison with jpeg"):
  with members:
    - "nonlocal low rank tensor factor analysis for image restoration"
    - "block wise lensless compressive camera"
    - "fundamental distortion limits of analog to digital compression"
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "compressive hyperspectral imaging with side information"
    - "tree structure bayesian compressive sensing for video"
    - "from denoising to compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "diffusion adaptation framework for compressive sensing reconstruction"):
  with members:
    - "adaptive diffusion schemes for heterogeneous networks"
    - "diffusion lms strategies in sensor networks with noisy input data"
    - "sparse distributed learning based on diffusion adaptation"
    - "diffusion adaptation over networks"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "sketching for large scale learning of mixture models"):
  with members:
    - "recycling randomness with structure for sublinear time kernel expansions"
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "fastmmd ensemble of circular discrepancy for efficient two sample test"
    - "a la carte learning fast kernels"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "orthogonal matching pursuit with replacement"

Connected through hyperedge HE-7 (induced by source paper "compressive k means"):
  with members:
    - "sketching for large scale learning of mixture models"
    - "greedy column subset selection new bounds and distributed algorithms"
    - "compressive spectral clustering"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "accelerated spectral clustering using graph filtering of random signals"
    - "exact solutions to super resolution on semi algebraic domains in higher dimensions"
    - "a unified framework for approximating and clustering data"
    - "random projections for k means clustering"

Connected through hyperedge HE-8 (induced by source paper "total variation regularized tensor rpca for background subtraction from compressive measurements"):
  with members:
    - "compressive sensing of sparse tensors"
    - "compressed sensing with prior information optimal strategies geometry and bounds"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "local convergence of an algorithm for subspace identification from partial data"
    - "surveillance video processing using compressive sensing"
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "online robust subspace tracking from partial information"
    - "online identification and tracking of subspaces from highly incomplete information"

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)