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: "structured compressed sensing from theory to applications"
  Abstract: Compressed sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Previous review articles in CS limit their scope to standard discrete-to-discrete measurement architectures using matrices of randomized nature and signal models based on standard sparsity. In recent years, CS has worked its way into several new application areas. This, in turn, necessitates a fresh look on many of the basics of CS. The random matrix measurement operator must be replaced by more structured sensing architectures that correspond to the characteristics of feasible acquisition hardware. The standard sparsity prior has to be extended to include a much richer class of signals and to encode broader data models, including continuous-time signals. In our overview, the theme is exploiting signal and measurement structure in compressive sensing. The prime focus is bridging theory and practice; that is, to pinpoint the potential of structured CS strategies to emerge from the math to the hardware. Our summary highlights new directions as well as relations to more traditional CS, with the hope of serving both as a review to practitioners wanting to join this emerging field, and as a reference for researchers that attempts to put some of the existing ideas in perspective of practical applications.

Connected through hyperedge HE-1 (induced by source paper "serial quantization for sparse time sequences"):
  with members:
    - "learning distributions from their samples under communication constraints"
    - "information theoretic and algorithmic thresholds for group testing"
    - "efficient data collection over multiple access wireless sensors network"
    - "efficient nonrandom construction and decoding of non adaptive group testing"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "distributed quantization for measurement of correlated sparse sources over noisy channels"
    - "distributed compressed sensing for static and time varying networks"

Connected through hyperedge HE-2 (induced by source paper "channel estimation and hybrid precoding for distributed phased arrays based mimo wireless communications"):
  with members:
    - "modeling and analyzing millimeter wave cellular systems"
    - "framework of channel estimation for hybrid analog and digital processing enabled massive mimo communications"
    - "5g cellular user equipment from theory to practical hardware design"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "codebook design for millimeter wave channel estimation with hybrid precoding structure"
    - "the world s first real time testbed for massive mimo design implementation and validation"
    - "an overview of transmission theory and techniques of large scale antenna systems for 5g wireless communications"

Connected through hyperedge HE-3 (induced by source paper "do log factors matter on optimal wavelet approximation and the foundations of compressed sensing"):
  with members:
    - "uniform recovery in infinite dimensional compressed sensing and applications to structured binary sampling"
    - "on oracle type local recovery guarantees in compressed sensing"
    - "robustness to unknown error in sparse regularization"
    - "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "on the absence of the rip in real world applications of compressed sensing and the rip in levels"

Connected through hyperedge HE-4 (induced by source paper "serial quantization for representing sparse signals"):
  with members:
    - "learning distributions from their samples under communication constraints"
    - "hardware limited task based quantization"
    - "efficient data collection over multiple access wireless sensors network"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "spatial compressive sensing for mimo radar"
    - "group testing algorithms bounds and simulations"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "sampling of graph signals via randomized local aggregations"):
  with members:
    - "greedy sampling of graph signals"
    - "the dual graph shift operator identifying the support of the frequency domain"
    - "graph based compression of dynamic 3d point cloud sequences"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "random sampling of bandlimited signals on graphs"
    - "discrete signal processing on graphs sampling theory"

Connected through hyperedge HE-6 (induced by source paper "random quadratic forms with dependence applications to restricted isometry and beyond"):
  with members:
    - "structured linear contextual bandits a sharp and geometric smoothed analysis"
    - "mitigating bias in adaptive data gathering via differential privacy"
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "leveraged volume sampling for linear regression"
    - "subsampling for ridge regression via regularized volume sampling"
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "randomized algorithms for matrices and data"
    - "a sparse johnson lindenstrauss transform"

Connected through hyperedge HE-7 (induced by source paper "compressed sensing channel estimation for ofdm with non gaussian multipath gains"):
  with members:
    - "fast channel estimation for millimetre wave wireless systems using overlapped beam patterns"
    - "average performance of orthogonal matching pursuit omp for sparse approximation"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "channel estimation for millimeter wave massive mimo with hybrid precoding over frequency selective fading channels"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "compressed sensing algorithms for ofdm channel estimation"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "limited feedback hybrid precoding for multi user millimeter wave systems"

Connected through hyperedge HE-8 (induced by source paper "an adaptive bayesian framework for recovery of sources with structured sparsity"):
  with members:
    - "oversampled adaptive sensing with random projections analysis and algorithmic approaches"
    - "theoretical bounds on map estimation in distributed sensing networks"
    - "near optimal adaptive compressed sensing"
    - "block sparse signals uncertainty relations and efficient recovery"

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)