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 "sparse signal recovery by ell_q minimization under restricted isometry property"):
  Abstract: In the context of compressed sensing, the nonconvex    ${\ell_q}$   minimization with   $0    has been studied in recent years. In this letter, by generalizing the sharp bound for   ${\ell_1}$   minimization of Cai and Zhang, we show that the condition    ${\delta_{({s^q} + 1)k}}    in terms of restricted isometry constant (RIC) can guarantee the exact recovery of   $k$  -sparse signals in the noiseless case and the stable recovery of approximately    $k$  -sparse signals in the noisy case by   ${\ell_q}$   minimization. This result is more general than the sharp bound for    ${\ell_1}$   minimization when the order of RIC is greater than   $2k$    and illustrates the fact that a better approximation to   ${\ell_0}$   minimization is provided by   ${\ell_q}$    minimization than that provided by    ${\ell_1}$   minimization.

Includes members:
  Member "new ric bounds via l_q minimization with 0 q 1 in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "performance guarantees for schatten p quasi norm minimization in recovery of low rank matrices")
  Member "sparse representation of a polytope and recovery of sparse signals and low rank matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the importance of phase in complex compressive sensing")
  Member "sharp rip bound for sparse signal and low rank matrix recovery":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval")

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)