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 model uncertainties in compressed sensing with application to convolutions and sporadic communication"):
  Abstract: The success of the compressed sensing paradigm has shown that a substantial reduction in sampling and storage complexity can be achieved in certain linear and non-adaptive estimation problems. It is therefore an advisable strategy for noncoherent information retrieval in, for example, sporadic blind and semi-blind communication and sampling problems. But, the conventional model is not practical here since the compressible signals have to be estimated from samples taken solely on the output of an un-calibrated system which is unknown during measurement but often compressible. Conventionally, one has either to operate at suboptimal sampling rates or the recovery performance substantially suffers from the dominance of model mismatch. In this work we discuss such type of estimation problems and we focus on bilinear inverse problems. We link this problem to the recovery of low-rank and sparse matrices and establish stable low-dimensional embeddings of the uncalibrated receive signals whereby addressing also efficient communication-oriented methods like universal random demodulation. Exemplarily, we investigate in more detail sparse convolutions serving as a basic communication channel model. In using some recent results from additive combinatorics we show that such type of signals can be efficiently low-rate sampled by semi-blind methods. Finally, we present a further application of these results in the field of phase retrieval from intensity Fourier measurements.

Includes members:
  Member "identifiability scaling laws in bilinear inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "complete dictionary recovery over the sphere i overview and the geometric picture")
  Member "on the stability of sparse convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stable blind deconvolution over the reals from additional autocorrelations")
  Member "ripless compressed sensing from anisotropic measurements":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "infinite dimensional compressed sensing from anisotropic measurements and applications to inverse problems in pde")
  Member "stable recovery from the magnitude of symmetrized fourier measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "blind deconvolution with additional autocorrelations via convex programs")
  Member "fundamentals of throughput maximization with random arrivals for m2m communications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint energy and sinr coverage in spatially clustered rf powered iot network")
  Member "sharp rip bound for sparse signal and low rank matrix recovery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval")
  Member "near optimal compressed sensing of sparse rank one matrices via sparse power factorization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the non convex geometry of low rank matrix optimization")
  Member "simultaneously structured models with application to sparse and low rank matrices":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "joint activity detection and channel estimation for mmw thz wideband massive access")

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)