human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "rounding sum of squares relaxations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "correcting camera shake by incremental sparse approximation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressed sensing without sparsity assumptions")
  Member "estimating unknown sparsity in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressive spectrum sensing for cognitive radio networks")
  Member "ldpc codes for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparsification of matrices and compressed sensing")
  Member "one bit compressed sensing by linear programming":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "performance analysis of sparse recovery based on constrained minimal singular values":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse recovery based on q ratio constrained minimal singular values")
  Member "restricted strong convexity and weighted matrix completion optimal bounds with noise":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "the stability of low rank matrix reconstruction a constrained singular value view":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sparse recovery based on q ratio constrained minimal singular values")

Question: Please tell me which class the source paper 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)