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: "time delay estimation from low rate samples a union of subspaces approach"
  Abstract: Time-delay estimation arises in many applications in which a multipath medium has to be identified from pulses transmitted through the channel. Previous methods for time delay recovery either operate on the analog received signal, or require sampling at the Nyquist rate of the transmitted pulse. In this paper, we develop a unified approach to time delay estimation from low-rate samples. This problem can be formulated in the broader context of sampling over an infinite union of subspaces. Although sampling over unions of subspaces has been receiving growing interest, previous results either focus on unions of finite-dimensional subspaces, or finite unions. The framework we develop here leads to perfect recovery of the multipath delays from samples of the channel output at the lowest possible rate, even in the presence of overlapping transmitted pulses, and allows for a variety of different sampling methods. The sampling rate depends only on the number of multipath components and the transmission rate, but not on the bandwidth of the probing signal. This result can be viewed as a sampling theorem over an infinite union of infinite dimensional subspaces. By properly manipulating the low-rate samples, we show that the time delays can be recovered using the well-known ESPRIT algorithm. Combining results from sampling theory with those obtained in the context of direction of arrival estimation, we develop sufficient conditions on the transmitted pulse and the sampling functions in order to ensure perfect recovery of the channel parameters at the minimal possible rate.

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 "restricted isometry property of gaussian random projection for finite set of subspaces"):
  with members:
    - "subspace clustering of dimensionality reduced data"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "achievable angles between two compressed sparse vectors under norm distance constraints imposed by the restricted isometry property a plane geometry approach"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"
    - "stable manifold embeddings with structured random matrices"
    - "structured compressed sensing from theory to applications"

Connected through hyperedge HE-3 (induced by source paper "sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit"):
  with members:
    - "a sharp recovery condition for block sparse signals by block orthogonal multi matching pursuit"
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "sharp conditions for exact support recovery of sparse signals with noise via omp"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "relaxed recovery conditions for omp ols by exploiting both coherence and decay"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "perturbation analysis of orthogonal matching pursuit"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"

Connected through hyperedge HE-4 (induced by source paper "cascade compressed carrier and doa estimation"):
  with members:
    - "sub nyquist sampling bridging theory and practice"
    - "rank awareness in joint sparse recovery"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-5 (induced by source paper "a tdoa technique with super resolution based on the volume cross correlation function"):
  with members:
    - "multichannel sampling of pulse streams at the rate of innovation"

Connected through hyperedge HE-6 (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-7 (induced by source paper "compressive parameter estimation for sparse translation invariant signals using polar interpolation"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "compressive parameter estimation in awgn"
    - "spectral compressive sensing with polar interpolation"
    - "compressive sensing for spread spectrum receivers"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"
    - "coherence pattern guided compressive sensing with unresolved grids"
    - "noise folding in compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "super resolution in phase space"):
  with members:
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "super resolution via superset selection and pruning"
    - "xampling at the rate of innovation"
    - "towards a mathematical theory of super resolution"

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)