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: "performance analysis of sparse recovery based on constrained minimal singular values"
  Abstract: The stability of sparse signal reconstruction with respect to measurement noise is investigated in this paper. We design efficient algorithms to verify the sufficient condition for unique l1 sparse recovery. One of our algorithms produces comparable results with the state-of-the-art technique and performs orders of magnitude faster. We show that the l1 -constrained minimal singular value (l1-CMSV) of the measurement matrix determines, in a very concise manner, the recovery performance of l1-based algorithms such as the Basis Pursuit, the Dantzig selector, and the LASSO estimator. Compared to performance analysis involving the Restricted Isometry Constant, the arguments in this paper are much less complicated and provide more intuition on the stability of sparse signal recovery. We show also that, with high probability, the subgaussian ensemble generates measurement matrices with l1-CMSVs bounded away from zero, as long as the number of measurements is relatively large. To compute the l1-CMSV and its lower bound, we design two algorithms based on the interior point algorithm and the semidefinite relaxation.

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"
    - "the stability of low rank matrix reconstruction a constrained singular value view"

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"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "the stability of low rank matrix reconstruction a constrained singular value view"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"

Connected through hyperedge HE-4 (induced by source paper "solve select scale a three step process for sparse signal estimation"):
  with members:
    - "estimating unknown sparsity in compressed sensing"
    - "comparing measures of sparsity"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-5 (induced by source paper "compressed sensing without sparsity assumptions"):
  with members:
    - "rounding sum of squares relaxations"
    - "correcting camera shake by incremental sparse approximation"
    - "estimating unknown sparsity in compressed sensing"
    - "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"
    - "new bounds for restricted isometry constants"
    - "comparing measures of sparsity"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-6 (induced by source paper "recovery of signals with low density"):
  with members:
    - "relaxed recovery conditions for omp ols by exploiting both coherence and decay"
    - "uncertainty relations and sparse signal recovery for pairs of general signal sets"
    - "on the theorem of uniform recovery of random sampling matrices"
    - "recovery of sparsely corrupted signals"

Connected through hyperedge HE-7 (induced by source paper "sparsity aware learning and compressed sensing an overview"):
  with members:
    - "experiments with random projection"
    - "compressive principal component pursuit"
    - "stable image reconstruction using total variation minimization"
    - "recovering low rank matrices from few coefficients in any basis"
    - "online sparse system identification and signal reconstruction using projections onto weighted ell_ 1 balls"
    - "structured compressed sensing from theory to applications"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "concentration based guarantees for low rank matrix reconstruction"

Connected through hyperedge HE-8 (induced by source paper "multi structural signal recovery for biomedical compressive sensing"):
  with members:
    - "multi sparse signal recovery for compressive sensing"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "matrix completion with noise"
    - "comparing measures of sparsity"

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)