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: "distortion rate function of sub nyquist sampled gaussian sources"
  Abstract: The amount of information lost in sub-Nyquist sampling of a continuous-time Gaussian stationary process is quantified. We consider a combined source coding and sub-Nyquist reconstruction problem in which the input to the encoder is a noisy sub-Nyquist sampled version of the analog source. We first derive an expression for the mean squared error in the reconstruction of the process from a noisy and information rate-limited version of its samples. This expression is a function of the sampling frequency and the average number of bits describing each sample. It is given as the sum of two terms: minimum mean square error in estimating the source from its noisy but otherwise fully observed sub-Nyquist samples, and a second term obtained by reverse waterfilling over an average of spectral densities associated with the polyphase components of the source. We extend this result to multi-branch uniform sampling, where the samples are available through a set of parallel channels with a uniform sampler and a pre-sampling filter in each branch. Further optimization to reduce distortion is then performed over the pre-sampling filters, and an optimal set of pre-sampling filters associated with the statistics of the input signal and the sampling frequency is found. This results in an expression for the minimal possible distortion achievable under any analog-to-digital conversion scheme involving uniform sampling and linear filtering. These results thus unify the Shannon–Whittaker–Kotelnikov sampling theorem and Shannon rate-distortion theory for Gaussian sources.

Connected through hyperedge HE-1 (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"
    - "fundamental distortion limits of analog to digital compression"
    - "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-2 (induced by source paper "system aware compression"):
  with members:
    - "optimized pre compensating compression"
    - "restoration by compression"
    - "the little engine that could regularization by denoising red"
    - "postprocessing of compressed images via sequential denoising"
    - "poisson inverse problems by the plug and play scheme"

Connected through hyperedge HE-3 (induced by source paper "reconstructing gaussian sources by spatial sampling"):
  with members:
    - "sampling rate distortion"
    - "near optimal sensor placement for linear inverse problems"
    - "lossy computing of correlated sources with fractional sampling"
    - "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing"
    - "distributed source coding in dense sensor networks"

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"
    - "fundamental distortion limits of analog to digital compression"
    - "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 "fundamental distortion limits of analog to digital compression"):
  with members:
    - "sampling rate distortion"
    - "indirect rate distortion function of a binary i i d source"
    - "channel capacity under sub nyquist nonuniform sampling"
    - "sampling and distortion tradeoffs for bandlimited periodic signals"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "lossy computing of correlated sources with fractional sampling"
    - "optimal phase transitions in compressed sensing"
    - "high resolution distributed sampling of bandlimited fields with low precision sensors"

Connected through hyperedge HE-6 (induced by source paper "sampling rate distortion"):
  with members:
    - "near optimal sensor placement for linear inverse problems"
    - "dass distributed adaptive sparse sensing"
    - "intersensor collaboration in distributed quantization networks"
    - "lossy computing of correlated sources with fractional sampling"
    - "sampling high dimensional bandlimited fields on low dimensional manifolds"
    - "optimal phase transitions in compressed sensing"
    - "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing"
    - "fundamentals of large sensor networks connectivity capacity clocks and computation"

Connected through hyperedge HE-7 (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"
    - "fundamental distortion limits of analog to digital compression"
    - "sampling rate distortion"
    - "the distortion rate function of cyclostationary gaussian processes"

Connected through hyperedge HE-8 (induced by source paper "sampling and distortion tradeoffs for indirect source retrieval"):
  with members:
    - "sampling rate distortion"
    - "compressive sampling of ensembles of correlated signals"
    - "sampling and distortion tradeoffs for bandlimited periodic signals"
    - "time stampless adaptive nonuniform sampling for stochastic signals"

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)