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: "compressed sensing and affine rank minimization under restricted isometry"
  Abstract: This paper establishes new restricted isometry conditions for compressed sensing and affine rank minimization. It is shown for compressed sensing that δ kA +θ k , kA   k  sparse signals in the noiseless case through the constrained  l  1  minimization. Furthermore, the upper bound 1 is sharp in the sense that for any e > 0, the condition δ kA  + θ k , kA   rM +θ r , rM   r  can be reconstructed exactly in the noiseless case via the constrained nuclear norm minimization; and for any e > 0, δ rM  +θ r , rM   kA +θ k , kA   rM +θ r , rM  <; 1 are also sufficient for the stable recovery of sparse signals and low-rank matrices respectively. Applications and extensions are also discussed.

Connected through hyperedge HE-1 (induced by source paper "truncated sparse approximation property and truncated q norm minimization"):
  with members:
    - "stable low rank matrix recovery via null space properties"
    - "nonlinear frames and sparse reconstructions in banach spaces"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "compressive multiplexing of correlated signals"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "sparse approximation property and stable recovery of sparse signals from noisy measurements"
    - "sparse signal reconstruction via iterative support detection"
    - "rank awareness in joint sparse recovery"

Connected through hyperedge HE-2 (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"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "sparse approximation property and stable recovery of sparse signals from noisy measurements"
    - "new bounds for restricted isometry constants"

Connected through hyperedge HE-3 (induced by source paper "an overview of low rank matrix recovery from incomplete observations"):
  with members:
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "poisson matrix recovery and completion"
    - "self calibration and biconvex compressive sensing"
    - "collaboratively learning preferences from ordinal data"
    - "categorical matrix completion"
    - "incoherence optimal matrix completion"

Connected through hyperedge HE-4 (induced by source paper "recovery of signals under the condition on ric and roc via prior support information"):
  with members:
    - "recovery of signals under the high order rip condition via prior support information"
    - "recovery analysis for weighted ell_1 minimization using a null space property"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "time invariant error bounds for modified cs based sparse signal sequence recovery"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "weighted ell_1 minimization with multiple weighting sets"
    - "recovering compressively sampled signals using partial support information"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-5 (induced by source paper "recovery of signals under the high order rip condition via prior support information"):
  with members:
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "recovering compressively sampled signals using partial support information"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "modified basis pursuit denoising modified bpdn for noisy compressive sensing with partially known support"
    - "weighted ell_1 minimization for sparse recovery with prior information"
    - "new bounds for restricted isometry constants"

Connected through hyperedge HE-6 (induced by source paper "improved analysis for subspace pursuit algorithm in terms of restricted isometry constant"):
  with members:
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "oblique pursuits for compressed sensing"
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "improved rip analysis of orthogonal matching pursuit"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "l_ 1 2 regularization convergence of iterative half thresholding algorithm"):
  with members:
    - "hidden cliques and the certification of the restricted isometry property"
    - "structured compressed sensing from theory to applications"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "sparse representation of a polytope and recovery of sparse signals and low rank matrices"):
  with members:
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "on the theorem of uniform recovery of random sampling matrices"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "new bounds for restricted isometry constants"

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)