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: "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information"
  Abstract: This paper offers a characterization of fundamental limits on the classification and reconstruction of high-dimensional signals from low-dimensional features, in the presence of side information. We consider a scenario where a decoder has access both to linear features of the signal of interest and to linear features of the side information signal; while the side information may be in a compressed form, the objective is recovery or classification of the primary signal, not the side information. The signal of interest and the side information are each assumed to have (distinct) latent discrete labels; conditioned on these two labels, the signal of interest and side information are drawn from a multivariate Gaussian distribution that correlates the two. With joint probabilities on the latent labels, the overall signal-(side information) representation is defined by a Gaussian mixture model. By considering bounds to the misclassification probability associated with the recovery of the underlying signal label, and bounds to the reconstruction error associated with the recovery of the signal of interest itself, we then provide sharp sufficient and/or necessary conditions for these quantities to approach zero when the covariance matrices of the Gaussians are nearly low rank. These conditions, which are reminiscent of the well-known Slepian–Wolf and Wyner–Ziv conditions, are the function of the number of linear features extracted from signal of interest, the number of linear features extracted from the side information signal, and the geometry of these signals and their interplay. Moreover, on assuming that the signal of interest and the side information obey such an approximately low-rank model, we derive the expansions of the reconstruction error as a function of the deviation from an exactly low-rank model; such expansions also allow the identification of operational regimes, where the impact of side information on signal reconstruction is most relevant. Our framework, which offers a principled mechanism to integrate side information in high-dimensional data problems, is also tested in the context of imaging applications. In particular, we report state-of-the-art results in compressive hyperspectral imaging applications, where the accompanying side information is a conventional digital photograph.

Connected through hyperedge HE-1 (induced by source paper "an approximate message passing framework for side information"):
  with members:
    - "an analysis of state evolution for approximate message passing with side information"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "finite sample analysis of approximate message passing algorithms"
    - "state evolution for approximate message passing with non separable functions"
    - "rapid fading due to human blockage in pedestrian crowds at 5g millimeter wave frequencies"
    - "mutual information and optimality of approximate message passing in random linear estimation"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "optimal trade offs in multi processor approximate message passing"

Connected through hyperedge HE-2 (induced by source paper "multi modal image processing based on coupled dictionary learning"):
  with members:
    - "multi modal dictionary learning for image separation with application in art investigation"
    - "x ray image separation via coupled dictionary learning"
    - "hyperspectral pansharpening a review"
    - "collaborative multi sensor classification via sparsity based representation"
    - "sparse modeling for image and vision processing"

Connected through hyperedge HE-3 (induced by source paper "multimodal image denoising based on coupled dictionary learning"):
  with members:
    - "hyperspectral pansharpening a review"
    - "boosting of image denoising algorithms"
    - "sparse modeling for image and vision processing"

Connected through hyperedge HE-4 (induced by source paper "multimodal image super resolution via joint sparse representations induced by coupled dictionaries"):
  with members:
    - "multi modal dictionary learning for image separation with application in art investigation"
    - "accelerating the super resolution convolutional neural network"
    - "x ray image separation via coupled dictionary learning"
    - "accurate image super resolution using very deep convolutional networks"
    - "hyperspectral pansharpening a review"
    - "deeply recursive convolutional network for image super resolution"
    - "collaborative multi sensor classification via sparsity based representation"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "multi modal dictionary learning for image separation with application in art investigation"):
  with members:
    - "x ray image separation via coupled dictionary learning"
    - "compressed sensing with prior information optimal strategies geometry and bounds"
    - "compressed sensing with side information geometrical interpretation and performance bounds"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "bayesian crack detection in ultra high resolution multimodal images of paintings"
    - "exact recovery of sparsely used dictionaries"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-6 (induced by source paper "compressive hyperspectral imaging with side information"):
  with members:
    - "task driven adaptive statistical compressive sensing of gaussian mixture models"
    - "communications inspired projection design with application to compressive sensing"
    - "statistical compressed sensing of gaussian mixture models"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"
    - "blind compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "approximate message passing based compressed sensing reconstruction with generalized elastic net prior"):
  with members:
    - "compressive imaging via approximate message passing with image denoising"
    - "binary linear classification and feature selection via generalized approximate message passing"
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "compressed sensing with prior information optimal strategies geometry and bounds"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "from denoising to compressed sensing"
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "parameterless optimal approximate message passing"

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)