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: "channel capacity under sub nyquist nonuniform sampling"
  Abstract: This paper investigates the effect of sub-Nyquist sampling upon the capacity of an analog channel. The channel is assumed to be a linear time-invariant Gaussian channel, where perfect channel knowledge is available at both the transmitter and the receiver. We consider a general class of right-invertible time-preserving sampling methods which includes irregular nonuniform sampling, and characterize in closed form the channel capacity achievable by this class of sampling methods, under a sampling rate and power constraint. Our results indicate that the optimal sampling structures extract out the set of frequencies that exhibits the highest signal-to-noise ratio among all spectral sets of measure equal to the sampling rate. This can be attained through filterbank sampling with uniform sampling grid employed at each branch with possibly different rates, or through a single branch of modulation and filtering followed by uniform sampling. These results reveal that for a large class of channels, employing irregular nonuniform sampling sets, while are typi- cally complicated to realize in practice, does not provide capacity gain over uniform sampling sets with appropriate preprocessing. Our findings demonstrate that aliasing or scrambling of spectral components does not provide capacity gain in this scenario, which is in contrast to the benefits obtained from random mixing in spectrum-blind compressive sampling schemes.

Connected through hyperedge HE-1 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "machine learning using the variational predictive information bottleneck with a validation set"
    - "on the difference between the information bottleneck and the deep information bottleneck"
    - "extracting robust and accurate features via a robust information bottleneck"
    - "general information bottleneck objectives and their applications to machine learning"
    - "vector gaussian ceo problem under logarithmic loss"
    - "on universal features for high dimensional learning and inference"
    - "estimation efficiency under privacy constraints"

Connected through hyperedge HE-2 (induced by source paper "the capacity of memoryless channels with sampled cyclostationary gaussian noise"):
  with members:
    - "analog to digital cognitive radio sampling detection and hardware"
    - "on the capacity of mimo broadband power line communications channels"
    - "sub nyquist cyclostationary detection for cognitive radio"
    - "on the capacity of narrowband plc channels"
    - "frequency shift filtering for ofdm signal recovery in narrowband power line communications"
    - "what will 5g be"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "on the minimax capacity loss under sub nyquist universal sampling"

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"
    - "fundamental distortion limits of analog to digital compression"
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "the distortion rate function of cyclostationary gaussian processes"
    - "speech recognition with deep recurrent neural networks"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-4 (induced by source paper "fundamental distortion limits of analog to digital compression"):
  with members:
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "sampling rate distortion"
    - "indirect rate distortion function of a binary i i d source"
    - "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-5 (induced by source paper "distortion rate function of sub nyquist sampled gaussian sources"):
  with members:
    - "the distortion rate function of cyclostationary gaussian processes"
    - "minimax capacity loss under sub nyquist universal sampling"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-6 (induced by source paper "sampling stationary signals subject to bitrate constraints"):
  with members:
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "optimal phase transitions in compressed sensing"
    - "achieving the gaussian rate distortion function by prediction"

Connected through hyperedge HE-7 (induced by source paper "minimax capacity loss under sub nyquist universal sampling"):
  with members:
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "sampling rate distortion"
    - "backing off from infinity performance bounds via concentration of spectral measure for random mimo channels"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "living at the edge a large deviations approach to the outage mimo capacity"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "optimal phase transitions in compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "on the minimax capacity loss under sub nyquist universal sampling"):
  with members:
    - "sampling rate distortion"
    - "backing off from infinity performance bounds via concentration of spectral measure for random mimo channels"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "living at the edge a large deviations approach to the outage mimo capacity"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "increased capacity per unit cost by oversampling"
    - "condition numbers of gaussian random matrices"

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)