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: "from compression to compressed sensing"
  Abstract: Can compression algorithms be employed for recovering signals from their underdetermined set of linear measurements? Addressing this question is the first step towards applying compression algorithms for compressed sensing (CS). In this paper, we consider a family of compression algorithms $\mathcal{C}_r$, parametrized by rate $r$, for a compact class of signals $\mathcal{Q} \subset \mathds{R}^n$. The set of natural images and JPEG at different rates are examples of $\mathcal{Q}$ and $\mathcal{C}_r$, respectively. We establish a connection between the rate-distortion performance of $\mathcal{C}_r$, and the number of linear measurements required for successful recovery in CS. We then propose compressible signal pursuit (CSP) algorithm and prove that, with high probability, it accurately and robustly recovers signals from an underdetermined set of linear measurements. We also explore the performance of CSP in the recovery of infinite dimensional signals.

Connected through hyperedge HE-1 (induced by source paper "using black box compression algorithms for phase retrieval"):
  with members:
    - "linear spectral estimators and an application to phase retrieval"
    - "robust compressive phase retrieval via deep generative priors"
    - "phase retrieval via linear programming fundamental limits and algorithmic improvements"
    - "fundamental limits of phasemax for phase retrieval a replica analysis"
    - "the little engine that could regularization by denoising red"
    - "phasemax convex phase retrieval via basis pursuit"
    - "compression based compressed sensing"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"

Connected through hyperedge HE-2 (induced by source paper "rank minimization for snapshot compressive imaging"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "analyzing the group sparsity based on the rank minimization methods"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "compressive hyperspectral imaging with side information"
    - "group based sparse representation for image restoration"
    - "from denoising to compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "solving inverse problems via auto encoders"):
  with members:
    - "amp inspired deep networks for sparse linear inverse problems"
    - "deep convolutional neural network for inverse problems in imaging"
    - "deep fully connected networks for video compressive sensing"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "new approach to bayesian high dimensional linear regression"
    - "u net convolutional networks for biomedical image segmentation"
    - "a deep learning approach to structured signal recovery"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-4 (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"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "video compressive sensing for spatial multiplexing cameras using motion flow models"
    - "compressive hyperspectral imaging with side information"

Connected through hyperedge HE-5 (induced by source paper "compressive phase retrieval of structured signal"):
  with members:
    - "linear spectral estimators and an application to phase retrieval"
    - "robust compressive phase retrieval via deep generative priors"
    - "phase retrieval via linear programming fundamental limits and algorithmic improvements"
    - "fundamental limits of phasemax for phase retrieval a replica analysis"
    - "restoration by compression"
    - "the little engine that could regularization by denoising red"
    - "phasemax convex phase retrieval via basis pursuit"
    - "compression based compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "an efficient algorithm for compression based compressed sensing"):
  with members:
    - "compression based compressed sensing"
    - "new approach to bayesian high dimensional linear regression"
    - "from denoising to compressed sensing"
    - "minimum complexity pursuit for universal compressed sensing"
    - "model based compressive sensing"
    - "sampling and reconstructing signals from a union of linear subspaces"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "compression based compressed sensing"):
  with members:
    - "universal compressed sensing"
    - "minimum complexity pursuit for universal compressed sensing"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "sharp recovery bounds for convex demixing with applications"
    - "sampling and recovery of pulse streams"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "stable restoration and separation of approximately sparse signals"
    - "optimal phase transitions in compressed sensing"

Connected through hyperedge HE-8 (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"

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)