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: "estimation of sparse mimo channels with common support"
  Abstract: We consider the problem of estimating sparse communication channels in the MIMO context. In small to medium bandwidth communications, as in the current standards for OFDM and CDMA communication systems (with bandwidth up to 20 MHz), such channels are individually sparse and at the same time share a common support set. Since the underlying physical channels are inherently continuous-time, we propose a parametric sparse estimation technique based on finite rate of innovation (FRI) principles. Parametric estimation is especially relevant to MIMO communications as it allows for a robust estimation and concise description of the channels. The core of the algorithm is a generalization of conventional spectral estimation methods to multiple input signals with common support. We show the application of our technique for channel estimation in OFDM (uniformly/contiguous DFT pilots) and CDMA downlink (Walsh-Hadamard coded schemes). In the presence of additive white Gaussian noise, theoretical lower bounds on the estimation of sparse common support (SCS) channel parameters in Rayleigh fading conditions are derived. Finally, an analytical spatial channel model is derived, and simulations on this model in the OFDM setting show the symbol error rate (SER) is reduced by a factor 2 (0 dB of SNR) to 5 (high SNR) compared to standard non-parametric methods - e.g. lowpass interpolation.

Connected through hyperedge HE-1 (induced by source paper "sampling and super resolution of sparse signals beyond the fourier domain"):
  with members:
    - "a sampling framework for solving physics driven inverse source problems"
    - "on unlimited sampling"
    - "shift invariant and sampling spaces associated with the special affine fourier transform"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "frida fri based doa estimation for arbitrary array layouts"
    - "a theory of super resolution from short time fourier transform measurements"
    - "identification of linear time varying systems through waveform diversity"
    - "super resolution in phase space"

Connected through hyperedge HE-2 (induced by source paper "on the sample complexity of multichannel frequency estimation via convex optimization"):
  with members:
    - "sparse methods for direction of arrival estimation"
    - "atomic norm minimization for modal analysis from random and compressed samples"
    - "compressive spectral estimation with single snapshot esprit stability and resolution"
    - "the restricted isometry property of subsampled fourier matrices"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "enhancing sparsity and resolution via reweighted atomic norm minimization"

Connected through hyperedge HE-3 (induced by source paper "decentralized joint sparse signal recovery a sparse bayesian learning approach"):
  with members:
    - "decentralized subspace pursuit for joint sparsity pattern recovery"
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "wideband spectrum sensing for cognitive radio networks a survey"
    - "cooperative sparsity pattern recovery in distributed networks via distributed omp"
    - "a fast distributed proximal gradient method"
    - "fast distributed gradient methods"
    - "optimization with sparsity inducing penalties"
    - "distributed compressive sensing"

Connected through hyperedge HE-4 (induced by source paper "on the support recovery of jointly sparse gaussian sources using sparse bayesian learning"):
  with members:
    - "on the restricted isometry of the columnwise khatri rao product"
    - "joint sparse recovery method for compressed sensing with structured dictionary mismatches"
    - "subspace methods for joint sparse recovery"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "rank awareness in joint sparse recovery"
    - "distributed compressive sensing"
    - "performance analysis for sparse support recovery"

Connected through hyperedge HE-5 (induced by source paper "low complexity message passing based massive mimo channel estimation by exploiting unknown sparse common support with dirichlet process"):
  with members:
    - "massive mimo for next generation wireless systems"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"

Connected through hyperedge HE-6 (induced by source paper "sampling without time recovering echoes of light via temporal phase retrieval"):
  with members:
    - "intensity only optical compressive imaging using a multiply scattering material and a double phase retrieval approach"
    - "resolving multipath interference in time of flight imaging via modulation frequency diversity and sparse regularization"

Connected through hyperedge HE-7 (induced by source paper "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"):
  with members:
    - "two stage subspace constrained precoding in massive mimo cellular systems"
    - "pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels"
    - "massive mimo with non ideal arbitrary arrays hardware scaling laws and circuit aware design"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "five disruptive technology directions for 5g"
    - "effects of channel aging in massive mimo systems"

Connected through hyperedge HE-8 (induced by source paper "a scalable framework for csi feedback in fdd massive mimo via dl path aligning"):
  with members:
    - "pilot decontamination via pdp alignment"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
    - "flexible pilot contamination mitigation with doppler psd alignment"
    - "structured compressive sensing based superimposed pilot design in downlink large scale mimo systems"
    - "achievable rates of fdd massive mimo systems with spatial channel correlation"
    - "pilot beam pattern design for channel estimation in massive mimo systems"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"

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.NI(Networking and Internet Architecture)