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 by non convex optimization instance optimality"
  Abstract: In this note, we address the theoretical properties of $\Delta_p$, a class of compressed sensing decoders that rely on $\ell^p$ minimization with 0<p<1 to recover estimates of sparse and compressible signals from incomplete and inaccurate measurements. In particular, we extend the results of Candes, Romberg and Tao, and Wojtaszczyk regarding the decoder $\Delta_1$, based on $\ell^1$ minimization, to $\Delta_p$ with 0<p<1. Our results are two-fold. First, we show that under certain sufficient conditions that are weaker than the analogous sufficient conditions for $\Delta_1$ the decoders $\Delta_p$ are robust to noise and stable in the sense that they are (2,p) instance optimal for a large class of encoders. Second, we extend the results of Wojtaszczyk to show that, like $\Delta_1$, the decoders $\Delta_p$ are (2,2) instance optimal in probability provided the measurement matrix is drawn from an appropriate distribution.

Connected through hyperedge HE-1 (induced by source paper "reconstruction error bounds for compressed sensing under poisson noise using the square root of the jensen shannon divergence"):
  with members:
    - "poisson matrix recovery and completion"
    - "the generalized lasso with non linear observations"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "performance bounds for expander based compressed sensing in poisson noise"
    - "stable restoration and separation of approximately sparse signals"
    - "learning exponential families in high dimensions strong convexity and sparsity"

Connected through hyperedge HE-2 (induced by source paper "quantization of compressive samples with stable and robust recovery"):
  with members:
    - "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"
    - "quantization and compressive sensing"
    - "noise shaping quantization methods for frame based and compressive sampling systems"
    - "recovery analysis for weighted ell_1 minimization using a null space property"
    - "an rip based approach to sigma delta quantization for compressed sensing"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "near optimal encoding for sigma delta quantization of finite frame expansions"
    - "sigma delta quantization of sub gaussian frame expansions and its application to compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "does ell_p minimization outperform ell_1 minimization"):
  with members:
    - "from denoising to compressed sensing"
    - "lifting l q optimization thresholds"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"

Connected through hyperedge HE-4 (induced by source paper "noise shaping quantization methods for frame based and compressive sampling systems"):
  with members:
    - "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"
    - "quantization of compressive samples with stable and robust recovery"
    - "an rip based approach to sigma delta quantization for compressed sensing"
    - "near optimal encoding for sigma delta quantization of finite frame expansions"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "sigma delta quantization of sub gaussian frame expansions and its application to compressed sensing"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "lower bounds for the error decay incurred by coarse quantization schemes"

Connected through hyperedge HE-5 (induced by source paper "performance guarantees for schatten p quasi norm minimization in recovery of low rank matrices"):
  with members:
    - "sparse signal recovery by ell_q minimization under restricted isometry property"
    - "doa estimation in partially correlated noise using low rank sparse matrix decomposition"
    - "calibration using matrix completion with application to ultrasound tomography"
    - "new ric bounds via l_q minimization with 0 q 1 in compressed sensing"
    - "matrix completion in colocated mimo radar recoverability bounds theoretical guarantees"
    - "on ric bounds of compressed sensing matrices for approximating sparse solutions using l q quasi norms"
    - "collaborative spectrum sensing from sparse observations in cognitive radio networks"
    - "matrix completion with noise"

Connected through hyperedge HE-6 (induced by source paper "non local patch regression robust image denoising in patch space"):
  with members:
    - "robust computation of linear models by convex relaxation"
    - "on the convergence of the irls algorithm in non local patch regression"
    - "non local euclidean medians"

Connected through hyperedge HE-7 (induced by source paper "non convex compressed sensing using partial support information"):
  with members:
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "recovering compressively sampled signals using partial support information"

Connected through hyperedge HE-8 (induced by source paper "the convergence guarantees of a non convex approach for sparse recovery"):
  with members:
    - "robustness of sparse recovery via f minimization a topological viewpoint"
    - "proof of convergence and performance analysis for sparse recovery via zero point attracting projection"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "sparse recovery using smoothed ell 0 sl0 convergence analysis"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "a stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"

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)