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: "unknown sparsity in compressed sensing denoising and inference"
  Abstract: The theory of compressed sensing (CS) asserts that an unknown signal    $x\in \mathbb {R} ^{p}$    can be accurately recovered from an underdetermined set of    $n$    linear measurements with    $n\ll p$    provided that    $x$    is sufficiently sparse. However, in applications, the degree of sparsity    $\|x\|_{0}$    is typically unknown, and the problem of directly estimating    $\|x\|_{0}$    has been a longstanding gap between theory and practice. A closely related issue is that    $\|x\|_{0}$    is a highly idealized measure of sparsity, and for real signals with entries not equal to 0, the value    $\|x\|_{0}=p$    is not a useful description of compressibility. In our previous conference paper  [1]  that examined these problems, we considered an alternative measure of soft sparsity,    $\|x\|_{1}^{2}/\|x\|_{2}^{2}$   , and designed a procedure to estimate    $\|x\|_{1}^{2}/\|x\|_{2}^{2}$    that does not rely on sparsity assumptions. This paper offers a new deconvolution-based method for estimating unknown sparsity, which has wider applicability and sharper theoretical guarantees. In particular, we introduce a family of entropy-based sparsity measures    $s_{q}(x)=\big ({\|x\|_{q}}/{\|x\|_{1}}\big )^{({q}/{1-q})}$    parameterized by    $q\in [0,\infty ]$   . This family interpolates between    $\|x\|_{0}=s_{0}(x)$    and    $\|x\|_{1}^{2}/\|x\|_{2}^{2}=s_{2}(x)$    as    $q$    ranges over [0, 2]. For any    $q\in (0,2]\setminus \{1\}$   , we propose an estimator    $\hat {s}_{q}(x)$    whose relative error converges at the dimension-free rate of    $1/\sqrt {n}$   , even when    $p/n\to \infty $   . Our main results also describe the limiting distribution of    $\hat {s}_{q}(x)$   , as well as some connections to basis pursuit denosing, the Lasso, deterministic measurement matrices, and inference problems in CS.

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"
    - "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"
    - "performance analysis of sparse recovery based on constrained minimal singular values"

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"
    - "estimating unknown sparsity in compressed sensing"
    - "performance analysis of sparse recovery based on constrained minimal singular values"
    - "the stability of low rank matrix reconstruction a constrained singular value view"

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)