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: "fundamental distortion limits of analog to digital compression"
  Abstract: Representing a continuous-time signal by a set of samples is a classical problem in signal processing. We study this problem under the additional constraint that the samples are quantized or compressed in a lossy manner under a limited bitrate budget. To this end, we consider a combined sampling and source coding problem in which an analog stationary Gaussian signal is reconstructed from its encoded samples. These samples are obtained by a set of bounded linear functionals of the continuous-time path, with a limitation on the average number of samples obtained per unit time available in this setting. We provide a full characterization of the minimal distortion in terms of the sampling frequency, the bitrate, and the signal's spectrum. Assuming that the signal's energy is not uniformly distributed over its spectral support, we show that for each compression bitrate there exists a critical sampling frequency smaller than the Nyquist rate, such that the distortion in signal reconstruction when sampling at this frequency is minimal. Our results can be seen as an extension of the classical sampling theorem for bandlimited random processes in the sense that it describes the minimal amount of excess distortion in the reconstruction due to lossy compression of the samples, and provides the minimal sampling frequency required in order to achieve this distortion. Finally, we compare the fundamental limits in the combined source coding and sampling problem to the performance of pulse code modulation (PCM), where each sample is quantized by a scalar quantizer using a fixed number of bits.

Connected through hyperedge HE-1 (induced by source paper "gaussian approximation of quantization error for estimation from compressed data"):
  with members:
    - "mean estimation from one bit measurements"
    - "learning distributions from their samples under communication constraints"
    - "refined asymptotics for rate distortion using gaussian codebooks for arbitrary sources"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "the distortion rate function of sampled wiener processes"
    - "compress and estimate in multiterminal source coding"
    - "a universal coding scheme for remote generation of continuous random variables"
    - "the rate distortion risk in estimation from compressed data"

Connected through hyperedge HE-2 (induced by source paper "hardware limited task based quantization"):
  with members:
    - "analog to digital compression a new paradigm for converting signals to bits"
    - "performance analysis for channel estimation with 1 bit adc and unknown quantization threshold"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "resolution adaptive hybrid mimo architectures for millimeter wave communications"
    - "hybrid analog digital beamforming for massive mimo systems"
    - "narrowband channel estimation for hybrid beamforming millimeter wave communication systems with one bit quantization"
    - "hybrid architectures with few bit adc receivers achievable rates and energy rate tradeoffs"
    - "millimeter wave receiver efficiency a comprehensive comparison of beamforming schemes with low resolution adcs"

Connected through hyperedge HE-3 (induced by source paper "analog to digital compression a new paradigm for converting signals to bits"):
  with members:
    - "the distortion rate function of sampled wiener processes"
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "the distortion rate function of cyclostationary gaussian processes"
    - "channel capacity under sub nyquist nonuniform sampling"
    - "speech recognition with deep recurrent neural networks"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-4 (induced by source paper "image compression based on compressive sensing end to end comparison with jpeg"):
  with members:
    - "nonlocal low rank tensor factor analysis for image restoration"
    - "block wise lensless compressive camera"
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "compressive hyperspectral imaging with side information"
    - "tree structure bayesian compressive sensing for video"
    - "from denoising to compressed sensing"
    - "compressive measurements generated by structurally random matrices asymptotic normality and quantization"

Connected through hyperedge HE-5 (induced by source paper "tradeoffs between convergence speed and reconstruction accuracy in inverse problems"):
  with members:
    - "learning to learn by gradient descent by gradient descent"
    - "maximal sparsity with deep networks"
    - "onsager corrected deep learning for sparse linear inverse problems"
    - "accelerating eulerian fluid simulation with convolutional networks"
    - "sampling stationary signals subject to bitrate constraints"
    - "raisr rapid and accurate image super resolution"
    - "a picture is worth a billion bits real time image reconstruction from dense binary pixels"
    - "sharp time data tradeoffs for linear inverse problems"

Connected through hyperedge HE-6 (induced by source paper "the distortion rate function of sampled wiener processes"):
  with members:
    - "analog to digital compression a new paradigm for converting signals to bits"
    - "remote estimation of the wiener process over a channel with random delay"
    - "compress and estimate in multiterminal source coding"
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "sampling rate distortion"
    - "the distortion rate function of cyclostationary gaussian processes"

Connected through hyperedge HE-7 (induced by source paper "oblivious fronthaul constrained relay for a gaussian channel"):
  with members:
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "oblivious processing in a fronthaul constrained gaussian channel"
    - "remarks on the most informative function conjecture at fixed mean"
    - "lower bounds and approximations for the information rate of the isi channel"
    - "on the capacity of the dither quantized gaussian channel"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "capacity of the discrete time awgn channel under output quantization"
    - "distributed mimo receiver achievable rates and upper bounds"

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)