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: "segmented compressed sampling for analog to information conversion method and performance analysis"
  Abstract: A new segmented compressed sampling (CS) method for analog-to-information conversion (AIC) is proposed. An analog signal measured by a number of parallel branches of mixers and integrators (BMIs), each characterized by a specific random sampling waveform, is first segmented in time into segments. Then the subsamples collected on different segments and different BMIs are reused so that a larger number of samples (at most ) than the number of BMIs is collected. This technique is shown to be equivalent to extending the measurement matrix, which consists of the BMI sampling waveforms, by adding new rows without actually increasing the number of BMIs. We prove that the extended measurement matrix satisfies the restricted isometry property with overwhelming probability if the original measurement matrix of BMI sampling waveforms satisfies it. We also prove that the signal recovery performance can be improved if our segmented CS-based AIC is used for sampling instead of the conventional AIC with the same number of BMIs. Therefore, the reconstruction quality can be improved by slightly increasing (by times) the sampling rate per each BMI. Simulation results verify the effectiveness of the proposed segmented CS method and the validity of our theoretical results. Particularly, our simulation results show significant signal recovery performance improvement when the segmented CS-based AIC is used instead of the conventional AIC with the same number of BMIs.

Connected through hyperedge HE-1 (induced by source paper "gridless quadrature compressive sampling with interpolated array technique"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "quadrature compressive sampling for radar signals"
    - "joint sparse recovery method for compressed sensing with structured dictionary mismatches"
    - "super resolution compressed sensing an iterative reweighted algorithm for joint parameter learning and sparse signal recovery"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"
    - "sparsity cognizant total least squares for perturbed compressive sampling"

Connected through hyperedge HE-2 (induced by source paper "segment sliding reconstruction of pulsed radar echoes with sub nyquist sampling"):
  with members:
    - "quadrature compressive sampling for radar signals"
    - "sparse recovery of streaming signals using l1 homotopy"
    - "sub nyquist radar via doppler focusing"
    - "xampling signal acquisition and processing in union of subspaces"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-3 (induced by source paper "ultra low complexity detection of spectrum holes in compressed wideband spectrum sensing"):
  with members:
    - "wideband spectrum sensing for cognitive radio networks a survey"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "enhanced compressive wideband frequency spectrum sensing for dynamic spectrum access"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "performance limits of segmented compressive sampling correlated measurements versus bits"):
  with members:
    - "approximate sparsity pattern recovery information theoretic lower bounds"
    - "information theoretic bounds for compressed sensing"
    - "shannon theoretic limits on noisy compressive sampling"

Connected through hyperedge HE-5 (induced by source paper "quadrature compressive sampling for radar signals"):
  with members:
    - "concentration of measure inequalities for toeplitz matrices with applications"
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "noise folding in compressed sensing"
    - "xampling signal acquisition and processing in union of subspaces"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-6 (induced by source paper "permutation meets parallel compressed sensing how to relax restricted isometry property for 2d sparse signals"):
  with members:
    - "progressive compressed sensing and reconstruction of multidimensional signals using hybrid transform prediction sparsity model"
    - "theoretical and empirical results for recovery from multiple measurements"

Connected through hyperedge HE-7 (induced by source paper "chaotic analog to information conversion with chaotic state modulation"):
  with members:
    - "a compressed sensing framework of frequency sparse signals through chaotic system"
    - "compressed sensing with nonlinear observations and related nonlinear optimisation problems"
    - "sparse recovery from nonlinear measurements with applications in bad data detection for power networks"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-8 (induced by source paper "pulse doppler signal processing with quadrature compressive sampling"):
  with members:
    - "sub nyquist radar via doppler focusing"
    - "identification of parametric underspread linear systems and super resolution radar"

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)