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: "signal space cosamp for sparse recovery with redundant dictionaries"
  Abstract: Compressive sensing (CS) has recently emerged as a powerful framework for acquiring sparse signals. The bulk of the CS literature has focused on the case where the acquired signal has a sparse or compressible representation in an orthonormal basis. In practice, however, there are many signals that cannot be sparsely represented or approximated using an orthonormal basis, but that do have sparse representations in a redundant dictionary. Standard results in CS can sometimes be extended to handle this case provided that the dictionary is sufficiently incoherent or well-conditioned, but these approaches fail to address the case of a truly redundant or overcomplete dictionary. In this paper we describe a variant of the iterative recovery algorithm CoSaMP for this more challenging setting. We utilize the D-RIP, a condition on the sensing matrix analogous to the well-known restricted isometry property. In contrast to prior work, the method and analysis are "signal-focused"; that is, they are oriented around recovering the signal rather than its dictionary coefficients. Under the assumption that we have a near-optimal scheme for projecting vectors in signal space onto the model family of candidate sparse signals, we provide provable recovery guarantees. Developing a practical algorithm that can provably compute the required near-optimal projections remains a significant open problem, but we include simulation results using various heuristics that empirically exhibit superior performance to traditional recovery algorithms.

Connected through hyperedge HE-1 (induced by source paper "data driven algorithm selection and parameter tuning two case studies in optimization and signal processing"):
  with members:
    - "oboe collaborative filtering for automl model selection"
    - "convolutional neural networks with alternately updated clique"
    - "learning to branch"
    - "adabatch efficient gradient aggregation rules for sequential and parallel stochastic gradient methods"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "learning to learn by gradient descent by gradient descent"
    - "big batch sgd automated inference using adaptive batch sizes"
    - "densely connected convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "greedy signal space recovery algorithm with overcomplete dictionaries in compressive sensing"):
  with members:
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "inexact gradient projection and fast data driven compressed sensing"):
  with members:
    - "sharp time data tradeoffs for linear inverse problems"
    - "compressive source separation theory and methods for hyperspectral imaging"
    - "matrix recipes for hard thresholding methods"
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "convergence rates of inexact proximal gradient methods for convex optimization"
    - "noise folding in compressed sensing"
    - "compressed sensing how sharp is the restricted isometry property"
    - "model based compressive sensing"

Connected through hyperedge HE-4 (induced by source paper "preserving injectivity under subgaussian mappings and its application to compressed sensing"):
  with members:
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "the gap between the null space property and the restricted isometry property"
    - "robust width a characterization of uniformly stable and robust compressed sensing"
    - "a null space analysis of the l1 synthesis method in dictionary based compressed sensing"
    - "performance analysis of ell_1 synthesis with coherent frames"
    - "sparse recovery with orthogonal matching pursuit under rip"

Connected through hyperedge HE-5 (induced by source paper "one bit compressive sensing of dictionary sparse signals"):
  with members:
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "quantization of compressive samples with stable and robust recovery"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "sampling and reconstructing signals from a union of linear subspaces"

Connected through hyperedge HE-6 (induced by source paper "spark level sparsity and the ell_1 tail minimization"):
  with members:
    - "a null space analysis of the l1 synthesis method in dictionary based compressed sensing"
    - "can we allow linear dependencies in the dictionary in the sparse synthesis framework"
    - "compressed sensing with general frames via optimal dual based ell _ 1 analysis"
    - "sparse signal reconstruction via iterative support detection"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-7 (induced by source paper "compressive sensing with redundant dictionaries and structured measurements"):
  with members:
    - "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"
    - "sampling in the analysis transform domain"
    - "analysis ell_1 recovery with frames and gaussian measurements"
    - "stable image reconstruction using total variation minimization"
    - "super resolution from noisy data"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "towards a mathematical theory of super resolution"
    - "compressed sensing with general frames via optimal dual based ell _ 1 analysis"

Connected through hyperedge HE-8 (induced by source paper "approximation algorithms for model based compressive sensing"):
  with members:
    - "on model based rip 1 matrices"
    - "combinatorial selection and least absolute shrinkage via the clash algorithm"
    - "sublinear time approximate model based sparse recovery for all"
    - "structured compressed sensing from theory to applications"
    - "optimization with sparsity inducing penalties"
    - "lower bounds for sparse recovery"
    - "model based compressive sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"

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)