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: "estimating unknown sparsity in compressed sensing"
  Abstract: In the theory of compressed sensing (CS), the sparsity ||x||_0 of the unknown signal x\in\R^p is commonly assumed to be a known parameter. However, it is typically unknown in practice. Due to the fact that many aspects of CS depend on knowing ||x||_0, it is important to estimate this parameter in a data-driven way. A second practical concern is that ||x||_0 is a highly unstable function of x. In particular, for real signals with entries not exactly equal to 0, the value ||x||_0=p is not a useful description of the effective number of coordinates. In this paper, we propose to estimate a stable measure of sparsity s(x):=||x||_1^2/||x||_2^2, which is a sharp lower bound on ||x||_0. Our estimation procedure uses only a small number of linear measurements, does not rely on any sparsity assumptions, and requires very little computation. A confidence interval for s(x) is provided, and its width is shown to have no dependence on the signal dimension p. Moreover, this result extends naturally to the matrix recovery setting, where a soft version of matrix rank can be estimated with analogous guarantees. Finally, we show that the use of randomized measurements is essential to estimating s(x). This is accomplished by proving that the minimax risk for estimating s(x) with deterministic measurements is large when n<<p.

Connected through hyperedge HE-1 (induced by source paper "compressive spectrum sensing for cognitive radio networks"):
  with members:
    - "techniques for dealing with uncertainty in cognitive radio networks"
    - "double detector for sparse signal detection from one bit compressed sensing measurements"
    - "one bit compressive sensing of dictionary sparse signals"
    - "a survey of sparse representation algorithms and applications"
    - "spectrum occupancy measurement an autocorrelation based scanning technique using usrp"
    - "matched filter detection with dynamic threshold for cognitive radio networks"
    - "a bayesian network model of the bit error rate for cognitive radio networks"
    - "dynamic spectrum access in cognitive radio networks with rf energy harvesting"

Connected through hyperedge HE-2 (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"
    - "sparse volterra and polynomial regression models recoverability and estimation"
    - "one bit compressed sensing by linear programming"
    - "performance analysis of sparse recovery based on constrained minimal singular values"

Connected through hyperedge HE-3 (induced by source paper "recovery of structured signals with prior information via maximizing correlation"):
  with members:
    - "stable recovery of structured signals from corrupted sub gaussian measurements"
    - "compressed sensing with prior information via maximizing correlation"
    - "video compressive sensing for spatial multiplexing cameras using motion flow models"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "optimal incorporation of sparsity information by weighted l 1 optimization"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (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"
    - "performance analysis of sparse recovery based on constrained minimal singular values"
    - "the stability of low rank matrix reconstruction a constrained singular value view"

Connected through hyperedge HE-5 (induced by source paper "sparsity order estimation from a single compressed observation vector"):
  with members:
    - "coherence optimization and best complex antipodal spherical codes"

Connected through hyperedge HE-6 (induced by source paper "efficient spectrum availability information recovery for wideband dsa networks a weighted compressive sampling approach"):
  with members:
    - "weighted ell_1 minimization for sparse recovery under arbitrary prior information"
    - "achieving autonomous compressive spectrum sensing for cognitive radios"
    - "wideband spectrum sensing for cognitive radio networks a survey"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "recovering compressively sampled signals using partial support information"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-7 (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"
    - "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"
    - "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-8 (induced by source paper "solve select scale a three step process for sparse signal estimation"):
  with members:
    - "performance analysis of sparse recovery based on constrained minimal singular values"
    - "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)