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: "on the sensitivity of continuous time noncoherent fading channel capacity"
  Abstract: The noncoherent capacity of stationary discrete-time fading channels is known to be very sensitive to the fine details of the channel model. More specifically, the measure of the support of the fading-process power spectral density (PSD) determines if noncoherent capacity grows logarithmically in SNR or slower than logarithmically. Such a result is unsatisfactory from an engineering point of view, as the support of the PSD cannot be determined through measurements. The aim of this paper is to assess whether, for general continuous-time Rayleigh-fading channels, this sensitivity has a noticeable impact on capacity at SNR values of practical interest. #R##N#To this end, we consider the general class of band-limited continuous-time Rayleigh-fading channels that satisfy the wide-sense stationary uncorrelated-scattering (WSSUS) assumption and are, in addition, underspread. We show that, for all SNR values of practical interest, the noncoherent capacity of every channel in this class is close to the capacity of an AWGN channel with the same SNR and bandwidth, independently of the measure of the support of the scattering function (the two-dimensional channel PSD). Our result is based on a lower bound on noncoherent capacity, which is built on a discretization of the channel input-output relation induced by projecting onto Weyl-Heisenberg (WH) sets. This approach is interesting in its own right as it yields a mathematically tractable way of dealing with the mutual information between certain continuous-time random signals.

Connected through hyperedge HE-1 (induced by source paper "off the grid recovery of time and frequency shifts with multiple measurement vectors"):
  with members:
    - "exploiting prior information in block sparse signals"
    - "distribution aware block sparse recovery via convex optimization"
    - "super resolution mimo radar"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "super resolution radar"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"

Connected through hyperedge HE-2 (induced by source paper "unified capacity limit of non coherent wideband fading channels"):
  with members:
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "millimeter wave channel modeling and cellular capacity evaluation"
    - "noncoherent capacity of underspread fading channels"
    - "low snr capacity of noncoherent fading channels"
    - "capacity per unit energy of fading channels with a peak constraint"

Connected through hyperedge HE-3 (induced by source paper "super resolution mimo radar"):
  with members:
    - "a theory of super resolution from short time fourier transform measurements"
    - "refined analysis of sparse mimo radar"
    - "super resolution of positive sources the discrete setup"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "super resolution radar"
    - "atomic norm denoising with applications to line spectral estimation"
    - "identification of sparse linear operators"
    - "analysis of sparse mimo radar"

Connected through hyperedge HE-4 (induced by source paper "coding theorem and converse for abstract channels with time structure and memory"):
  with members:
    - "the szeg o asymptotics for doubly dispersive gaussian channels"
    - "directed information causal estimation and communication in continuous time"
    - "noncoherent capacity of underspread fading channels"
    - "on multipath fading channels at high snr"

Connected through hyperedge HE-5 (induced by source paper "super resolution radar"):
  with members:
    - "super resolution of positive sources the discrete setup"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "the recoverability limit for superresolution via sparsity"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "super resolution via superset selection and pruning"
    - "atomic norm denoising with applications to line spectral estimation"
    - "identification of sparse linear operators"

Connected through hyperedge HE-6 (induced by source paper "degrees of freedom of generic block fading mimo channels without a priori channel state information"):
  with members:
    - "generic correlation increases noncoherent mimo capacity"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "a lower bound on the noncoherent capacity pre log for the mimo channel with temporally correlated fading"
    - "capacity pre log of noncoherent simo channels via hironaka s theorem"
    - "on the capacity of large mimo block fading channels"
    - "noncoherent simo pre log via resolution of singularities"
    - "high snr capacity of wireless communication channels in the noncoherent setting a primer"
    - "the simo pre log can be larger than the siso pre log"

Connected through hyperedge HE-7 (induced by source paper "oversampling increases the pre log of noncoherent rayleigh fading channels"):
  with members:
    - "degrees of freedom of generic block fading mimo channels without a priori channel state information"
    - "on wiener phase noise channels at high signal to noise ratio"
    - "capacity pre log of noncoherent simo channels via hironaka s theorem"
    - "on the capacity of large mimo block fading channels"
    - "high snr capacity of wireless communication channels in the noncoherent setting a primer"
    - "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)