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: "graphical models concepts in compressed sensing"
  Abstract: This paper surveys recent work in applying ideas from graphical models and message passing algorithms to solve large scale regularized regression problems. In particular, the focus is on compressed sensing reconstruction via ell_1 penalized least-squares (known as LASSO or BPDN). We discuss how to derive fast approximate message passing algorithms to solve this problem. Surprisingly, the analysis of such algorithms allows to prove exact high-dimensional limit results for the LASSO risk. #R##N#This paper will appear as a chapter in a book on `Compressed Sensing' edited by Yonina Eldar and Gitta Kutyniok.

Connected through hyperedge HE-1 (induced by source paper "random access for massive machine type communications"):
  with members:
    - "beamwidth control for noma in hybrid mmwave communication systems"
    - "design of non orthogonal beamspace multiple access for cellular internet of things"
    - "on the performance gain of noma over oma in uplink single cell systems"
    - "multi beam noma for hybrid mmwave systems"
    - "joint pilot and payload power control for uplink mimo noma with mrc sic receivers"
    - "reliability based windowed decoding for spatially coupled ldpc codes"
    - "short packet downlink transmission with non orthogonal multiple access"
    - "a multi beam noma framework for hybrid mmwave systems"

Connected through hyperedge HE-2 (induced by source paper "sparse regression codes"):
  with members:
    - "the error probability of sparse superposition codes with approximate message passing decoding"
    - "sparcs for unsourced random access"
    - "spatially coupled sparse regression codes design and state evolution analysis"
    - "techniques for improving the finite length performance of sparse superposition codes"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "the rate distortion function and excess distortion exponent of sparse regression codes with optimal encoding"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"

Connected through hyperedge HE-3 (induced by source paper "the error probability of sparse superposition codes with approximate message passing decoding"):
  with members:
    - "finite sample analysis of approximate message passing algorithms"
    - "techniques for improving the finite length performance of sparse superposition codes"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"
    - "proof of threshold saturation for spatially coupled sparse superposition codes"
    - "phase transitions in sparse pca"
    - "information theoretically optimal sparse pca"

Connected through hyperedge HE-4 (induced by source paper "analysis of approximate message passing with non separable denoisers and markov random field priors"):
  with members:
    - "finite sample analysis of approximate message passing algorithms"
    - "analysis of approximate message passing with a class of non separable denoisers"
    - "state evolution for approximate message passing with non separable functions"
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "compressive hyperspectral imaging via approximate message passing"
    - "compressive imaging via approximate message passing with image denoising"
    - "from denoising to compressed sensing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "nonlinear function estimation with empirical bayes and approximate message passing"):
  with members:
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "empirical bayes and full bayes for signal estimation"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "sparse volterra and polynomial regression models recoverability and estimation"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "message passing algorithms for compressed sensing"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-6 (induced by source paper "passed spurious analysing descent algorithms and local minima in spiked matrix tensor model"):
  with members:
    - "marvels and pitfalls of the langevin algorithm in noisy high dimensional inference"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "depth creates no bad local minima"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"
    - "a statistical model for tensor pca"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "passed spurious descent algorithms and local minima in spiked matrix tensor models"):
  with members:
    - "marvels and pitfalls of the langevin algorithm in noisy high dimensional inference"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "depth creates no bad local minima"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "a simple derivation of amp and its state evolution via first order cancellation"):
  with members:
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "the mutual information in random linear estimation"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "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)