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 approximately sparse signals phase transitions and optimal reconstruction"
  Abstract: Compressed sensing is designed to measure sparse signals directly in a compressed form. However, most signals of interest are only “approximately sparse”, i.e. even though the signal contains only a small fraction of relevant (large) components the other components are not strictly equal to zero, but are only close to zero. In this paper we model the approximately sparse signal with a Gaussian distribution of small components, and we study its compressed sensing with dense random matrices. We use replica calculations to determine the mean-squared error of the Bayes-optimal reconstruction for such signals, as a function of the variance of the small components, the density of large components and the measurement rate. We then use the G-AMP algorithm and we quantify the region of parameters for which this algorithm achieves optimality (for large systems). Finally, we show that in the region where the G-AMP algorithm for the homogeneous measurement matrices is not optimal, a special “seeding” design of a spatially-coupled measurement matrix allows to restore optimality.

Connected through hyperedge HE-1 (induced by source paper "approximate message passing decoder and capacity achieving sparse superposition codes"):
  with members:
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "generalized approximate message passing decoder for universal sparse superposition codes"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "the mutual information in random linear estimation"
    - "proof of threshold saturation for spatially coupled sparse superposition codes"
    - "compressive phase retrieval via generalized approximate message passing"

Connected through hyperedge HE-2 (induced by source paper "intensity only optical compressive imaging using a multiply scattering material and a double phase retrieval approach"):
  with members:
    - "approximate message passing with restricted boltzmann machine priors"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "compressive phase retrieval via generalized approximate message passing"
    - "s amp approximate message passing for general matrix ensembles"
    - "phase retrieval via matrix completion"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"):
  with members:
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "compressive phase retrieval via generalized approximate message passing"
    - "properties of spatial coupling in compressed sensing"
    - "replica analysis and approximate message passing decoder for superposition codes"
    - "a simple proof of threshold saturation for coupled scalar recursions"
    - "subsampling at information theoretically optimal rates"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"

Connected through hyperedge HE-4 (induced by source paper "from denoising to compressed sensing"):
  with members:
    - "compressive imaging via approximate message passing with image denoising"
    - "does ell_p minimization outperform ell_1 minimization"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"
    - "bilinear generalized approximate message passing part i derivation"
    - "s amp approximate message passing for general matrix ensembles"
    - "signal recovery using expectation consistent approximation for linear observations"
    - "generalized approximate message passing for cosparse analysis compressive sensing"
    - "parameterless optimal approximate message passing"

Connected through hyperedge HE-5 (induced by source paper "robust error correction for real valued signals via message passing decoding and spatial coupling"):
  with members:
    - "a simple proof of threshold saturation for coupled scalar recursions"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "subsampling at information theoretically optimal rates"
    - "compressed sensing with sparse structured matrices"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "a power efficient sensing communication scheme joint source channel network coding by using compressive sensing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "the effect of spatial coupling on compressive sensing"

Connected through hyperedge HE-6 (induced by source paper "non adaptive pooling strategies for detection of rare faulty items"):
  with members:
    - "compressed sensing under matrix uncertainty optimum thresholds and robust approximate message passing"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"
    - "compressed sensing with sparse structured matrices"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "multiple access demodulation in the lifted signal graph with spatial coupling"
    - "improvement of bp based cdma multiuser detection by spatial coupling"

Connected through hyperedge HE-7 (induced by source paper "compressed sensing under matrix uncertainty optimum thresholds and robust approximate message passing"):
  with members:
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "sparsity cognizant total least squares for perturbed compressive sampling"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "message passing algorithms for compressed sensing"

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)