human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "bits from photons oversampled image acquisition using binary poisson statistics"
  Abstract: We study a new image sensor that is reminiscent of a traditional photographic film. Each pixel in the sensor has a binary response, giving only a 1-bit quantized measurement of the local light intensity. To analyze its performance, we formulate the oversampled binary sensing scheme as a parameter estimation problem based on quantized Poisson statistics. We show that, with a single-photon quantization threshold and large oversampling factors, the Cramer-Rao lower bound (CRLB) of the estimation variance approaches that of an ideal unquantized sensor, i.e., as if there were no quantization in the sensor measurements. Furthermore, the CRLB is shown to be asymptotically achievable by the maximum-likelihood estimator (MLE). By showing that the log-likelihood function of our problem is concave, we guarantee the global optimality of iterative algorithms in finding the MLE. Numerical results on both synthetic data and images taken by a prototype sensor verify our theoretical analysis and demonstrate the effectiveness of our image reconstruction algorithm. They also suggest the potential application of the oversampled binary sensing scheme in high dynamic range photography.

Connected through hyperedge HE-1 (induced by source paper "convolutional dictionary learning based auto encoders for natural exponential family distributions"):
  with members:
    - "scalable convolutional dictionary learning with constrained recurrent sparse auto encoders"
    - "handling incomplete heterogeneous data using vaes"
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "simple efficient and neural algorithms for sparse coding"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "deep networks for image super resolution with sparse prior"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "megapixel photon counting color imaging using quanta image sensor"):
  with members:
    - "optimal threshold design for quanta image sensor"
    - "a little bit too much high speed imaging from sparse photon counts"
    - "plug and play admm for image restoration fixed point convergence and applications"

Connected through hyperedge HE-3 (induced by source paper "color filter arrays for quanta image sensors"):
  with members:
    - "megapixel photon counting color imaging using quanta image sensor"
    - "optimal threshold design for quanta image sensor"

Connected through hyperedge HE-4 (induced by source paper "phase transitions of spectral initialization for high dimensional nonconvex estimation"):
  with members:
    - "optimal spectral initialization for signal recovery with applications to phase retrieval"
    - "phase retrieval via polytope optimization geometry phase transitions and new algorithms"
    - "fundamental limits of phasemax for phase retrieval a replica analysis"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"
    - "phasemax convex phase retrieval via basis pursuit"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"

Connected through hyperedge HE-5 (induced by source paper "plug and play admm for image restoration fixed point convergence and applications"):
  with members:
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "plug and play priors for bright field electron tomography and sparse interpolation"
    - "algorithm induced prior for image restoration"
    - "postprocessing of compressed images via sequential denoising"
    - "poisson inverse problems by the plug and play scheme"
    - "compressive imaging via approximate message passing with image denoising"
    - "group based sparse representation for image restoration"
    - "from denoising to compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "a picture is worth a billion bits real time image reconstruction from dense binary pixels"):
  with members:
    - "sparsity based poisson denoising with dictionary learning"
    - "learning efficient sparse and low rank models"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-7 (induced by source paper "image reconstruction from dense binary pixels"):
  with members:
    - "sparsity based poisson denoising with dictionary learning"
    - "learning efficient sparse and low rank models"

Connected through hyperedge HE-8 (induced by source paper "robust binary fused compressive sensing using adaptive outlier pursuit"):
  with members:
    - "binary fused compressive sensing 1 bit compressive sensing meets group sparsity"
    - "blind one bit compressive sampling"
    - "robust 1 bit compressive sensing via gradient support pursuit"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "statistical mechanics approach to 1 bit compressed sensing"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "a fast iterative algorithm for recovery of sparse signals from one bit quantized measurements"
    - "regime change bit depth versus measurement rate in compressive sensing"

Question: Please tell me which class the center node 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)