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 random measurements in signal processing"
  Abstract: Compressed sensing and its extensions have recently triggered interest in randomized signal acquisition. A key finding is that random measurements provide sparse signal reconstruction guarantees for efficient and stable algorithms with a minimal number of samples. While this was first shown for (unstructured) Gaussian random measurement matrices, applications require certain structure of the measurements leading to structured random measurement matrices. Near optimal recovery guarantees for such structured measurements have been developed over the past years in a variety of contexts. This article surveys the theory in three scenarios: compressed sensing (sparse recovery), low rank matrix recovery, and phaseless estimation. The random measurement matrices to be considered include random partial Fourier matrices, partial random circulant matrices (subsampled convolutions), matrix completion, and phase estimation from magnitudes of Fourier type measurements. The article concludes with a brief discussion of the mathematical techniques for the analysis of such structured random measurements.

Connected through hyperedge HE-1 (induced by source paper "sub gaussian matrices on sets optimal tail dependence and applications"):
  with members:
    - "phase retrieval under a generative prior"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "from compressed sensing to compressed bit streams practical encoders tractable decoders"
    - "why are deep nets reversible a simple theory with implications for training"
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "randomized sketches of convex programs with sharp guarantees"
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction with subgaussian matrices a unified theory"

Connected through hyperedge HE-2 (induced by source paper "swift link a compressive beam alignment algorithm for practical mmwave radios"):
  with members:
    - "partially blind handovers for mmwave new radio aided by sub 6 ghz lte signaling"
    - "a novel millimeter wave channel simulator and applications for 5g wireless communications"
    - "a compressive channel estimation technique robust to synchronization impairments"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "hierarchical codebook design for beamforming training in millimeter wave communication"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "hybrid architectures with few bit adc receivers achievable rates and energy rate tradeoffs"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"

Connected through hyperedge HE-3 (induced by source paper "analysis of frequency agile radar via compressed sensing"):
  with members:
    - "sparse doppler sensing based on nested arrays"
    - "spectrum sharing radar coexistence via xampling"
    - "refined analysis of sparse mimo radar"
    - "fundamental limits of hrr profiling and velocity compensation for stepped frequency waveforms"
    - "spatial compressive sensing for mimo radar"
    - "quadrature compressive sampling for radar signals"
    - "sub nyquist radar via doppler focusing"
    - "extended range profiling in stepped frequency radar with sparse recovery"

Connected through hyperedge HE-4 (induced by source paper "total variation minimization in compressed sensing"):
  with members:
    - "convex recovery of a structured signal from independent random linear measurements"
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "learning without concentration"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "guarantees of total variation minimization for signal recovery"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "spread spectrum magnetic resonance imaging"

Connected through hyperedge HE-5 (induced by source paper "an overview of low rank matrix recovery from incomplete observations"):
  with members:
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "poisson matrix recovery and completion"
    - "self calibration and biconvex compressive sensing"
    - "collaboratively learning preferences from ordinal data"
    - "categorical matrix completion"
    - "incoherence optimal matrix completion"

Connected through hyperedge HE-6 (induced by source paper "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"):
  with members:
    - "uniform recovery from subgaussian multi sensor measurements"
    - "compressed sensing and parallel acquisition"
    - "sparsity and parallel acquisition optimal uniform and nonuniform recovery guarantees"
    - "optimal sparse recovery for multi sensor measurements"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "refined analysis of sparse mimo radar"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "low rank tensor recovery via iterative hard thresholding"):
  with members:
    - "tensor theta norms and low rank recovery"
    - "the restricted isometry property of subsampled fourier matrices"
    - "stable low rank matrix recovery via null space properties"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "approximation algorithms for model based compressive sensing"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "low rank matrix recovery from rank one measurements"
    - "compressive multiplexing of correlated signals"

Connected through hyperedge HE-8 (induced by source paper "a partial derandomization of phaselift using spherical designs"):
  with members:
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "ripless compressed sensing from anisotropic measurements"
    - "phase retrieval from coded diffraction patterns"
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "phase retrieval stability and recovery guarantees"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"
    - "phase retrieval with polarization"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

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)