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 with structured sparsity and structured acquisition"
  Abstract: Compressed Sensing (CS) is an appealing framework for applications such as Magnetic Resonance Imaging (MRI). However, up-to-date, the sensing schemes suggested by CS theories are made of random isolated measurements, which are usually incompatible with the physics of acquisition. To reflect the physical constraints of the imaging device, we introduce the notion of blocks of measurements: the sensing scheme is not a set of isolated measurements anymore, but a set of groups of measurements which may represent any arbitrary shape (parallel or radial lines for instance). Structured acquisition with blocks of measurements are easy to implement, and provide good reconstruction results in practice. However, very few results exist on the theoretical guarantees of CS reconstructions in this setting. In this paper, we derive new CS results for structured acquisitions and signals satisfying a prior structured sparsity. The obtained results provide a recovery probability of sparse vectors that explicitly depends on their support. Our results are thus support-dependent and offer the possibility for flexible assumptions on the sparsity structure. Moreover, the results are drawing-dependent, since we highlight an explicit dependency between the probability of reconstructing a sparse vector and the way of choosing the blocks of measurements. Numerical simulations show that the proposed theory is faithful to experimental observations.

Connected through hyperedge HE-1 (induced by source paper "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis"):
  with members:
    - "linear reconstructions and the analysis of the stable sampling rate"
    - "uniform recovery in infinite dimensional compressed sensing and applications to structured binary sampling"
    - "multilevel illumination coding for fourier transform interferometry in fluorescence spectroscopy"
    - "compressive single pixel fourier transform imaging using structured illumination"
    - "compressive hyperspectral imaging fourier transform interferometry meets single pixel camera"
    - "on oracle type local recovery guarantees in compressed sensing"
    - "compressive imaging using fast transform coding"
    - "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"

Connected through hyperedge HE-2 (induced by source paper "do log factors matter on optimal wavelet approximation and the foundations of compressed sensing"):
  with members:
    - "uniform recovery in infinite dimensional compressed sensing and applications to structured binary sampling"
    - "on oracle type local recovery guarantees in compressed sensing"
    - "robustness to unknown error in sparse regularization"
    - "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"
    - "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"
    - "on the absence of the rip in real world applications of compressed sensing and the rip in levels"
    - "spread spectrum compressed sensing mri using chirp radio frequency pulses"

Connected through hyperedge HE-3 (induced by source paper "snapshot compressed sensing performance bounds and algorithms"):
  with members:
    - "rank minimization for snapshot compressive imaging"
    - "an efficient algorithm for compression based compressed sensing"
    - "deep fully connected networks for video compressive sensing"
    - "csvideonet a real time end to end learning framework for high frame rate video compressive sensing"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "video compressive sensing for spatial multiplexing cameras using motion flow models"
    - "compressive hyperspectral imaging with side information"
    - "from denoising to compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "on oracle type local recovery guarantees in compressed sensing"):
  with members:
    - "compressed sensing and parallel acquisition"
    - "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "living on the edge phase transitions in convex programs with random data"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "an algorithm for variable density sampling with block constrained acquisition"
    - "stable and robust sampling strategies for compressive imaging"
    - "on variable density compressive sampling"

Connected through hyperedge HE-5 (induced by source paper "a variable density sampling scheme for compressive fourier transform interferometry"):
  with members:
    - "multilevel illumination coding for fourier transform interferometry in fluorescence spectroscopy"
    - "compressive single pixel fourier transform imaging using structured illumination"
    - "on oracle type local recovery guarantees in compressed sensing"
    - "multispectral compressive imaging strategies using fabry p erot filtered sensors"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "an analysis of block sampling strategies in compressed sensing"
    - "a panorama on multiscale geometric representations intertwining spatial directional and frequency selectivity"

Connected through hyperedge HE-6 (induced by source paper "structured sampling and fast reconstruction of smooth graph signals"):
  with members:
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "subgraph based filterbanks for graph signals"
    - "compressive spectral clustering"
    - "signal recovery on graphs random versus experimentally designed sampling"
    - "accelerated spectral clustering using graph filtering of random signals"
    - "random sampling of bandlimited signals on graphs"
    - "discrete signal processing on graphs sampling theory"
    - "asymptotic justification of bandlimited interpolation of graph signals for semi supervised learning"

Connected through hyperedge HE-7 (induced by source paper "gelfand numbers related to structured sparsity and besov space embeddings with small mixed smoothness"):
  with members:
    - "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"
    - "on the absence of the rip in real world applications of compressed sensing and the rip in levels"
    - "uniform recovery of fusion frame structured sparse signals"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "one bit compressed sensing by linear programming"
    - "optimization with sparsity inducing penalties"
    - "block sparse signals uncertainty relations and efficient recovery"

Connected through hyperedge HE-8 (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"
    - "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"
    - "on the absence of the rip in real world applications of compressed sensing and the rip in levels"

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)