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: "the stability of low rank matrix reconstruction a constrained singular value view"
  Abstract: The stability of low-rank matrix reconstruction with respect to noise is investigated in this paper. The $\ell_*$-constrained minimal singular value ($\ell_*$-CMSV) of the measurement operator is shown to determine the recovery performance of nuclear norm minimization based algorithms. Compared with the stability results using the matrix restricted isometry constant, the performance bounds established using $\ell_*$-CMSV are more concise, and their derivations are less complex. Isotropic and subgaussian measurement operators are shown to have $\ell_*$-CMSVs bounded away from zero with high probability, as long as the number of measurements is relatively large. The $\ell_*$-CMSV for correlated Gaussian operators are also analyzed and used to illustrate the advantage of $\ell_*$-CMSV compared with the matrix restricted isometry constant. We also provide a fixed point characterization of $\ell_*$-CMSV that is potentially useful for its computation.

Connected through hyperedge HE-1 (induced by source paper "sparse recovery based on q ratio constrained minimal singular values"):
  with members:
    - "stable and robust ell_p constrained compressive sensing recovery via robust width property"
    - "unknown sparsity in compressed sensing denoising and inference"
    - "on robust width property for lasso and dantzig selector"
    - "robust width a characterization of uniformly stable and robust compressed sensing"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "estimating unknown sparsity in compressed sensing"
    - "sparse volterra and polynomial regression models recoverability and estimation"
    - "one bit compressed sensing by linear programming"

Connected through hyperedge HE-2 (induced by source paper "on q ratio cmsv for sparse recovery"):
  with members:
    - "sparse recovery based on q ratio constrained minimal singular values"
    - "unknown sparsity in compressed sensing denoising and inference"
    - "estimating unknown sparsity in compressed sensing"
    - "performance analysis of sparse recovery based on constrained minimal singular values"

Connected through hyperedge HE-3 (induced by source paper "unknown sparsity in compressed sensing denoising and inference"):
  with members:
    - "rounding sum of squares relaxations"
    - "correcting camera shake by incremental sparse approximation"
    - "estimating unknown sparsity in compressed sensing"
    - "ldpc codes for compressed sensing"
    - "one bit compressed sensing by linear programming"
    - "performance analysis of sparse recovery based on constrained minimal singular values"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"

Connected through hyperedge HE-4 (induced by source paper "estimating unknown sparsity in compressed sensing"):
  with members:
    - "performance analysis of sparse recovery based on constrained minimal singular values"
    - "compressed sensing performance bounds under poisson noise"
    - "new bounds for restricted isometry constants"
    - "exact matrix completion via convex optimization"
    - "comparing measures of sparsity"
    - "non negative matrix factorization with sparseness constraints"

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)