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: "from theory to practice sub nyquist sampling of sparse wideband analog signals"
  Abstract: Conventional sub-Nyquist sampling methods for analog signals exploit prior information about the spectral support. In this paper, we consider the challenging problem of blind sub-Nyquist sampling of multiband signals, whose unknown frequency support occupies only a small portion of a wide spectrum. Our primary design goals are efficient hardware implementation and low computational load on the supporting digital processing. We propose a system, named the modulated wideband converter, which first multiplies the analog signal by a bank of periodic waveforms. The product is then low-pass filtered and sampled uniformly at a low rate, which is orders of magnitude smaller than Nyquist. Perfect recovery from the proposed samples is achieved under certain necessary and sufficient conditions. We also develop a digital architecture, which allows either reconstruction of the analog input, or processing of any band of interest at a low rate, that is, without interpolating to the high Nyquist rate. Numerical simulations demonstrate many engineering aspects: robustness to noise and mismodeling, potential hardware simplifications, real-time performance for signals with time-varying support and stability to quantization effects. We compare our system with two previous approaches: periodic nonuniform sampling, which is bandwidth limited by existing hardware devices, and the random demodulator, which is restricted to discrete multitone signals and has a high computational load. In the broader context of Nyquist sampling, our scheme has the potential to break through the bandwidth barrier of state-of-the-art analog conversion technologies such as interleaved converters.

Connected through hyperedge HE-1 (induced by source paper "image guided depth sampling and reconstruction"):
  with members:
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "propagating confidences through cnns for sparse data regression"
    - "hms net hierarchical multi scale sparsity invariant network for sparse depth completion"
    - "estimating depth from rgb and sparse sensing"
    - "in defense of classical image processing fast depth completion on the cpu"
    - "deep convolutional compressed sensing for lidar depth completion"
    - "sparse to dense depth prediction from sparse depth samples and a single image"

Connected through hyperedge HE-2 (induced by source paper "joint sparse recovery using signal space matching pursuit"):
  with members:
    - "nearly optimal bounds for orthogonal least squares"
    - "compressed sensing for wireless communications useful tips and tricks"
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "statistical recovery of simultaneously sparse time varying signals from multiple measurement vectors"
    - "recovery of sparse signals via generalized orthogonal matching pursuit a new analysis"
    - "generalized orthogonal matching pursuit"
    - "subspace methods for joint sparse recovery"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"

Connected through hyperedge HE-3 (induced by source paper "secure and efficient compressed sensing based encryption with sparse matrices"):
  with members:
    - "indistinguishability and energy sensitivity of asymptotically gaussian compressed encryption"
    - "a survey on wireless security technical challenges recent advances and future trends"
    - "bi level protected compressive sampling"
    - "on known plaintext attacks to a compressed sensing based encryption a quantitative analysis"
    - "low complexity multiclass encryption by compressed sensing"
    - "mergeshuffle a very fast parallel random permutation algorithm"
    - "permutation meets parallel compressed sensing how to relax restricted isometry property for 2d sparse signals"
    - "compressive mechanism utilizing sparse representation in differential privacy"

Connected through hyperedge HE-4 (induced by source paper "exploiting prior information in block sparse signals"):
  with members:
    - "maximum a posteriori signal recovery with prior information applications to compressive sensing"
    - "performance limits for noisy multimeasurement vector problems"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "spectral super resolution with prior knowledge"
    - "living on the edge phase transitions in convex programs with random data"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "accelerating multi echo t2 weighted mr imaging analysis prior group sparse optimization"
    - "recovering compressively sampled signals using partial support information"

Connected through hyperedge HE-5 (induced by source paper "fast compressed power spectrum estimation towards a practical solution for wideband spectrum sensing"):
  with members:
    - "efficient spectrum availability information recovery for wideband dsa networks a weighted compressive sampling approach"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"
    - "wideband spectrum sensing for cognitive radio networks a survey"
    - "wideband spectrum sensing with sub nyquist sampling in cognitive radios"
    - "xampling signal acquisition and processing in union of subspaces"

Connected through hyperedge HE-6 (induced by source paper "compressive spectrum sensing for cognitive radio networks"):
  with members:
    - "techniques for dealing with uncertainty in cognitive radio networks"
    - "double detector for sparse signal detection from one bit compressed sensing measurements"
    - "one bit compressive sensing of dictionary sparse signals"
    - "a survey of sparse representation algorithms and applications"
    - "spectrum occupancy measurement an autocorrelation based scanning technique using usrp"
    - "matched filter detection with dynamic threshold for cognitive radio networks"
    - "a bayesian network model of the bit error rate for cognitive radio networks"
    - "dynamic spectrum access in cognitive radio networks with rf energy harvesting"

Connected through hyperedge HE-7 (induced by source paper "a universal sampling method for reconstructing signals with simple fourier transforms"):
  with members:
    - "relating leverage scores and density using regularized christoffel functions"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "active regression via linear sample sparsification"
    - "fourier sparse interpolation without a frequency gap"
    - "a robust sparse fourier transform in the continuous setting"
    - "principal component projection without principal component analysis"
    - "recursive sampling for the nystr om method"

Connected through hyperedge HE-8 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices"):
  with members:
    - "d oamp a denoising based signal recovery algorithm for compressed sensing"
    - "graded quantization for multiple description coding of compressive measurements"
    - "refined analysis of sparse mimo radar"
    - "operational rate distortion performance of single source and distributed compressed sensing"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "sparse recovery with orthogonal matching pursuit under rip"

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)