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: "recovery of sparsest signals via ell q minimization"
  Abstract: In this paper, it is proved that every $s$-sparse vector ${\bf x}\in {\mathbb R}^n$ can be exactly recovered from the measurement vector ${\bf z}={\bf A} {\bf x}\in {\mathbb R}^m$ via some $\ell^q$-minimization with $0< q\le 1$, as soon as each $s$-sparse vector ${\bf x}\in {\mathbb R}^n$ is uniquely determined by the measurement ${\bf z}$.

Connected through hyperedge HE-1 (induced by source paper "compressed sensing recovery via nonconvex shrinkage penalties"):
  with members:
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "sparse approximation property and stable recovery of sparse signals from noisy measurements"

Connected through hyperedge HE-2 (induced by source paper "nonlinear frames and sparse reconstructions in banach spaces"):
  with members:
    - "does ell_p minimization outperform ell_1 minimization"
    - "spatially distributed sampling and reconstruction"
    - "sampling and galerkin reconstruction in reproducing kernel spaces"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "phase retrieval stability and recovery guarantees"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "sparse approximation property and stable recovery of sparse signals from noisy measurements"

Connected through hyperedge HE-3 (induced by source paper "on the null space constant for ell _p minimization"):
  with members:
    - "the convergence guarantees of a non convex approach for sparse recovery"

Connected through hyperedge HE-4 (induced by source paper "sparse approximation property and stable recovery of sparse signals from noisy measurements"):
  with members:
    - "lower bounds for sparse recovery"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "the statistical restricted isometry property and the wigner semicircle distribution of incoherent dictionaries"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

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)