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 representation of a polytope and recovery of sparse signals and low rank matrices"):
  Abstract: This paper considers compressed sensing and affine rank minimization in both noiseless and noisy cases and establishes sharp restricted isometry conditions for sparse signal and low-rank matrix recovery. The analysis relies on a key technical tool which represents points in a polytope by convex combinations of sparse vectors. The technique is elementary while leads to sharp results. #R##N#It is shown that for any given constant $t\ge {4/3}$, in compressed sensing $\delta_{tk}^A  0$, $\delta_{tk}^A<\sqrt{\frac{t-1}{t}}+\epsilon$ is not sufficient to guarantee the exact recovery of all $k$-sparse signals for large $k$. Similar result also holds for matrix recovery. In addition, the conditions $\delta_{tk}^A < \sqrt{(t-1)/t}$ and $\delta_{tr}^\mathcal{M}< \sqrt{(t-1)/t}$ are also shown to be sufficient respectively for stable recovery of approximately sparse signals and low-rank matrices in the noisy case.

Includes members:
  Member "compressed sensing and affine rank minimization under restricted isometry":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "truncated sparse approximation property and truncated q norm minimization")
  Member "sharp rip bound for sparse signal and low rank matrix recovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval")
  Member "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressive spectrum sensing for cognitive radio networks")
  Member "on the theorem of uniform recovery of random sampling matrices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "matrix infinitely divisible series tail inequalities and applications in optimization")
  Member "beyond nyquist efficient sampling of sparse bandlimited signals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and efficient compressed sensing based encryption with sparse matrices")
  Member "new bounds for restricted isometry constants":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the use of mutual coherence to prove ell 1 ell 0 equivalence in classification problems")

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)