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: "shannon meets nyquist capacity of sampled gaussian channels"
  Abstract: We explore two fundamental questions at the intersection of sampling theory and information theory: how channel capacity is affected by sampling below the channel's Nyquist rate, and what sub-Nyquist sampling strategy should be employed to maximize capacity. In particular, we derive the capacity of sampled analog channels for three prevalent sampling strategies: sampling with filtering, sampling with filter banks, and sampling with modulation and filter banks. These sampling mechanisms subsume most nonuniform sampling techniques applied in practice. Our analyses illuminate interesting connections between undersampled channels and multiple-input multiple-output channels. The optimal sampling structures are shown to extract out the frequencies with the highest SNR from each aliased frequency set, while suppressing aliasing and out-of-band noise. We also highlight connections between undersampled channel capacity and minimum mean-squared error (MSE) estimation from sampled data. In particular, we show that the filters maximizing capacity and the ones minimizing MSE are equivalent under both filtering and filter-bank sampling strategies. These results demonstrate the effect upon channel capacity of sub-Nyquist sampling techniques, and characterize the tradeoff between information rate and sampling rate.

Connected through hyperedge HE-1 (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"
    - "channel capacity under sub nyquist nonuniform sampling"
    - "frequency shift filtering for ofdm signal recovery in narrowband power line communications"
    - "what will 5g be"
    - "on the minimax capacity loss under sub nyquist universal sampling"

Connected through hyperedge HE-2 (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"
    - "channel capacity under sub nyquist nonuniform sampling"
    - "sampling and distortion tradeoffs for bandlimited periodic signals"
    - "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-3 (induced by source paper "distortion rate function of sub nyquist sampled gaussian sources"):
  with members:
    - "the distortion rate function of cyclostationary gaussian processes"
    - "channel capacity under sub nyquist nonuniform sampling"
    - "minimax capacity loss under sub nyquist universal sampling"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-4 (induced by source paper "backing off from infinity performance bounds via concentration of spectral measure for random mimo channels"):
  with members:
    - "massive mimo for next generation wireless systems"
    - "bounds on the second order coding rate of the mimo rayleigh block fading channel"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "on the distribution of mimo mutual information an in depth painleve based characterization"
    - "outage capacity for the optical mimo channel"
    - "network mimo with linear zero forcing beamforming large system analysis impact of channel estimation and reduced complexity scheduling"
    - "achieving massive mimo spectral efficiency with a not so large number of antennas"
    - "signal processing in large systems a new paradigm"

Connected through hyperedge HE-5 (induced by source paper "oblivious fronthaul constrained relay for a gaussian channel"):
  with members:
    - "fundamental distortion limits of analog to digital compression"
    - "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"
    - "capacity of the discrete time awgn channel under output quantization"
    - "distributed mimo receiver achievable rates and upper bounds"

Connected through hyperedge HE-6 (induced by source paper "optimal fronthaul compression for synchronization in the uplink of cloud radio access networks"):
  with members:
    - "optimized backhaul compression for uplink cloud radio access network"
    - "uplink multicell processing with limited backhaul via per base station successive interference cancellation"
    - "robust and efficient distributed compression for cloud radio access networks"
    - "optimal channel training in uplink network mimo systems"

Connected through hyperedge HE-7 (induced by source paper "the distortion rate function of cyclostationary gaussian processes"):
  with members:
    - "compress and estimate in multiterminal source coding"
    - "distortion rate function of sub nyquist sampled gaussian sources"

Connected through hyperedge HE-8 (induced by source paper "channel capacity under sub nyquist nonuniform sampling"):
  with members:
    - "minimax capacity loss under sub nyquist universal sampling"
    - "channel capacity under general nonuniform sampling"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "optimal phase transitions in compressed sensing"
    - "sub nyquist sampling bridging theory and practice"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "increased capacity per unit cost by oversampling"

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)