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: "compressed sensing of analog signals in shift invariant spaces"
  Abstract: A traditional assumption underlying most data converters is that the signal should be sampled at a rate exceeding twice the highest frequency. This statement is based on a worst-case scenario in which the signal occupies the entire available bandwidth. In practice, many signals are sparse so that only part of the bandwidth is used. In this paper, we develop methods for low-rate sampling of continuous-time sparse signals in shift-invariant (SI) spaces, generated by m kernels with period T . We model sparsity by treating the case in which only k out of the m generators are active, however, we do not know which k are chosen. We show how to sample such signals at a rate much lower than m/T, which is the minimal sampling rate without exploiting sparsity. Our approach combines ideas from analog sampling in a subspace with a recently developed block diagram that converts an infinite set of sparse equations to a finite counterpart. Using these two components we formulate our problem within the framework of finite compressed sensing (CS) and then rely on algorithms developed in that context. The distinguishing feature of our results is that in contrast to standard CS, which treats finite-length vectors, we consider sampling of analog signals for which no underlying finite-dimensional model exists. The proposed framework allows to extend much of the recent literature on CS to the analog domain.

Connected through hyperedge HE-1 (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-2 (induced by source paper "successive concave sparsity approximation for compressed sensing"):
  with members:
    - "iterative concave rank approximation for recovering low rank matrices"
    - "doa estimation in partially correlated noise using low rank sparse matrix decomposition"
    - "recovery of low rank matrices under affine constraints via a smoothed rank function"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "on the stable recovery of the sparsest overcomplete representations in presence of noise"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "successive concave sparsity approximation near oracle performance in a wide range of sparsity levels"):
  with members:
    - "iterative concave rank approximation for recovering low rank matrices"
    - "recovery of low rank matrices under affine constraints via a smoothed rank function"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "on the stable recovery of the sparsest overcomplete representations in presence of noise"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "compressive link acquisition in multiuser communications"):
  with members:
    - "asynchronous code division random access using convex optimization"
    - "gps signal acquisition via compressive multichannel sampling"
    - "reduced dimension multiuser detection"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "dynamic updating for ell_ 1 minimization"
    - "on off random access channels a compressed sensing framework"

Connected through hyperedge HE-5 (induced by source paper "matched filtering from limited frequency samples"):
  with members:
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "restricted isometries for partial random circulant matrices"
    - "circulant and toeplitz matrices in compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "oblique pursuits for compressed sensing"):
  with members:
    - "subspace methods for joint sparse recovery"
    - "xampling signal acquisition and processing in union of subspaces"
    - "a probabilistic and ripless theory of compressed sensing"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"
    - "model based compressive sensing"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-7 (induced by source paper "reconciling compressive sampling systems for spectrally sparse continuous time signals"):
  with members:
    - "xampling signal acquisition and processing in union of subspaces"
    - "sub nyquist sampling of short pulses"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "model based compressive sensing"
    - "rank awareness in joint sparse recovery"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "efficient algorithms for robust recovery of images from compressed data"):
  with members:
    - "convex approaches to model wavelet sparsity patterns"
    - "a probabilistic and ripless theory of compressed sensing"
    - "model based compressive sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "kalman filtered compressed sensing"
    - "consistency of the group lasso and multiple kernel learning"

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)