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 "on the null space constant for ell _p minimization"):
  Abstract: The literature on sparse recovery often adopts the   ${\ell _p}$   “norm” (  $p \in [0,1]$   ) as the penalty to induce sparsity of the signal satisfying an underdetermined linear system. The performance of the corresponding   ${\ell _p}$   minimization problem can be characterized by its null space constant. In spite of the NP-hardness of computing the constant, its properties can still help in illustrating the performance of    ${\ell _p}$   minimization. In this letter, we show the strict increase of the null space constant in the sparsity level   $k$    and its continuity in the exponent   $p$  . We also indicate that the constant is strictly increasing in   $p$   with probability 1 when the sensing matrix    ${\bf A}$   is randomly generated. Finally, we show how these properties can help in demonstrating the performance of   ${\ell _p}$   minimization, mainly in the relationship between the the exponent   $p$    and the sparsity level   $k$  .

Includes members:
  Member "the convergence guarantees of a non convex approach for sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast and provable admm for learning with generative priors")
  Member "recovery of sparsest signals via ell q minimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressed sensing recovery via nonconvex shrinkage penalties")

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)