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: "spread spectrum magnetic resonance imaging"
  Abstract: We propose a novel compressed sensing technique to accelerate the magnetic resonance imaging (MRI) acquisition process. The method, coined spread spectrum MRI or simply s MRI, consists of premodulating the signal of interest by a linear chirp before random -space under-sampling, and then reconstructing the signal with nonlinear algorithms that promote sparsity. The effectiveness of the procedure is theoretically underpinned by the optimization of the coherence between the sparsity and sensing bases. The proposed technique is thoroughly studied by means of numerical simulations, as well as phantom and in vivo experiments on a 7T scanner. Our results suggest that s MRI performs better than state-of-the-art variable density -space under-sampling approaches.

Connected through hyperedge HE-1 (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"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "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"

Connected through hyperedge HE-2 (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"
    - "structured random measurements in signal processing"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "analyzing the structure of multidimensional compressed sensing problems through coherence"):
  with members:
    - "compressed sensing with structured sparsity and structured acquisition"
    - "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"
    - "on asymptotic incoherence and its implications for compressed sensing of inverse problems"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "stable and robust sampling strategies for compressive imaging"
    - "on variable density compressive sampling"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "compressed sensing with structured sparsity and structured acquisition"):
  with members:
    - "on the absence of the rip in real world applications of compressed sensing and the rip in levels"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "on minimal trajectories for mobile sampling of bandlimited fields"
    - "sampling and reconstruction of spatial fields using mobile sensors"
    - "exact recovery conditions for sparse representations with partial support information"
    - "an algorithm for variable density sampling with block constrained acquisition"
    - "performance bounds for grouped incoherent measurements in compressive sensing"
    - "stable and robust sampling strategies for compressive imaging"

Connected through hyperedge HE-5 (induced by source paper "modulated unit norm tight frames for compressed sensing"):
  with members:
    - "sparsity averaging for compressive imaging"
    - "convolutional compressed sensing using deterministic sequences"
    - "projection design for statistical compressive sensing a tight frame based approach"
    - "the restricted isometry property for random block diagonal matrices"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques"
    - "a probabilistic and ripless theory of compressed sensing"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-6 (induced by source paper "compressive imaging and characterization of sparse light deflection maps"):
  with members:
    - "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
    - "compressive source separation theory and methods for hyperspectral imaging"
    - "analysis ell_1 recovery with frames and gaussian measurements"
    - "compressive optical deflectometric tomography a constrained total variation minimization approach"
    - "theory and applications of compressed sensing"
    - "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques"
    - "compressive schlieren deflectometry"
    - "compressed sensing performance bounds under poisson noise"

Connected through hyperedge HE-7 (induced by source paper "breaking the coherence barrier a new theory for compressed sensing"):
  with members:
    - "a partial derandomization of phaselift using spherical designs"
    - "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"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
    - "on asymptotic incoherence and its implications for compressed sensing of inverse problems"
    - "lensless imaging by compressive sensing"
    - "the stone transform multi resolution image enhancement and real time compressive video"
    - "an analysis of block sampling strategies in compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "an analysis of block sampling strategies in compressed sensing"):
  with members:
    - "compressed sensing with structured sparsity and structured acquisition"
    - "a partial derandomization of phaselift using spherical designs"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "an algorithm for variable density sampling with block constrained acquisition"
    - "performance bounds for grouped incoherent measurements in compressive sensing"
    - "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques"
    - "stable and robust sampling strategies for compressive imaging"
    - "recovering low rank matrices from few coefficients in any basis"

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.OH(Other Computer Science)