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 "reconstruction error bounds for compressed sensing under poisson noise using the square root of the jensen shannon divergence"):
  Abstract: Reconstruction error bounds in compressed sensing under Gaussian or uniform bounded noise do not translate easily to the case of Poisson noise. Reasons for this include the signal dependent nature of Poisson noise, and also the fact that the negative log likelihood in case of a Poisson distribution (which is directly related to the generalized Kullback-Leibler divergence) is not a metric and does not obey the triangle inequality. There exist prior theoretical results in the form of provable error bounds for computationally tractable estimators for compressed sensing problems under Poisson noise. However, these results do not apply to \emph{realistic} compressive systems, which must obey some crucial constraints such as non-negativity and flux preservation. On the other hand, there exist provable error bounds for such realistic systems in the published literature, but they are for estimators that are computationally intractable. In this paper, we develop error bounds for a computationally tractable estimator which also applies to realistic compressive systems obeying the required constraints. The focus of our technique is on the replacement of the generalized Kullback-Leibler divergence with an information theoretic metric - namely the square root of the Jensen-Shannon divergence, which is related to a symmetrized version of the Poisson log likelihood function. This replacement allows for very simple proofs of the error bounds. Numerical experiments are performed showing the practical use of the technique in signal and image reconstruction from compressed measurements under Poisson noise. Our technique is applicable to signals that are sparse or compressible in any orthornormal basis, and works with high probability for any randomly generated sensing matrix that obeys the non-negativity and flux preservation constraints.

Includes members:
  Member "poisson matrix recovery and completion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reconstruction error bounds for compressed sensing under poisson or poisson gaussian noise using variance stabilization transforms")
  Member "the generalized lasso with non linear observations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery")
  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")
  Member "performance bounds for expander based compressed sensing in poisson noise":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "poisson matrix recovery and completion")
  Member "stable restoration and separation of approximately sparse signals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "boosting occluded image classification via subspace decomposition based estimation of deep features")
  Member "learning exponential families in high dimensions strong convexity and sparsity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regularized loss minimizers with local data perturbation consistency and data irrecoverability")
  Member "sparse recovery by non convex optimization instance optimality":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "quantization of compressive samples with stable and robust recovery")

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)