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: "message passing algorithms for compressed sensing"
  Abstract: Compressed sensing aims to undersample certain high-dimensional signals yet accurately reconstruct them by exploiting signal characteristics. Accurate reconstruction is possible when the object to be recovered is sufficiently sparse in a known basis. Currently, the best known sparsity–undersampling tradeoff is achieved when reconstructing by convex optimization, which is expensive in important large-scale applications. Fast iterative thresholding algorithms have been intensively studied as alternatives to convex optimization for large-scale problems. Unfortunately known fast algorithms offer substantially worse sparsity–undersampling tradeoffs than convex optimization. We introduce a simple costless modification to iterative thresholding making the sparsity–undersampling tradeoff of the new algorithms equivalent to that of the corresponding convex optimization procedures. The new iterative-thresholding algorithms are inspired by belief propagation in graphical models. Our empirical measurements of the sparsity–undersampling tradeoff for the new algorithms agree with theoretical calculations. We show that a state evolution formalism correctly derives the true sparsity–undersampling tradeoff. There is a surprising agreement between earlier calculations based on random convex polytopes and this apparently very different theoretical formalism.

Connected through hyperedge HE-1 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding"):
  with members:
    - "sparse regression codes"
    - "the error probability of sparse superposition codes with approximate message passing decoding"
    - "spatially coupled sparse regression codes design and state evolution analysis"
    - "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"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"

Connected through hyperedge HE-2 (induced by source paper "inference in multi layer networks with matrix valued unknowns"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "inference with deep generative priors in high dimensions"
    - "asymptotics of map inference in deep networks"
    - "entropy and mutual information in models of deep neural networks"
    - "sunlayer stable denoising with generative networks"
    - "correction by projection denoising images with generative adversarial networks"
    - "task aware compressed sensing with generative adversarial networks"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"

Connected through hyperedge HE-3 (induced by source paper "compressed coding amp based decoding and analog spatial coupling"):
  with members:
    - "analysis of approximate message passing with non separable denoisers and markov random field priors"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"
    - "state evolution for approximate message passing with non separable functions"
    - "generalized approximate message passing decoder for universal sparse superposition codes"
    - "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"
    - "spatially coupled ldpc codes constructed from protographs"

Connected through hyperedge HE-4 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity"):
  with members:
    - "grant free massive random access with a massive mimo receiver"
    - "sparse regression codes"
    - "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver"
    - "polar coding and random spreading for unsourced multiple access"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "state evolution for approximate message passing with non separable functions"
    - "mutual information and optimality of approximate message passing in random linear estimation"

Connected through hyperedge HE-5 (induced by source paper "phase transition analysis for covariance based massive random access with massive mimo"):
  with members:
    - "a dimension reduction based joint activity detection and channel estimation algorithm for massive access"
    - "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver"
    - "massive mimo unsourced random access"
    - "joint active user detection and channel estimation in massive access systems exploiting reed muller sequences"
    - "multi cell sparse activity detection for massive random access massive mimo versus cooperative mimo"
    - "improved scaling law for activity detection in massive mimo systems"
    - "grant free massive mtc enabled massive mimo a compressive sensing approach"
    - "sparse activity detection for massive connectivity"

Connected through hyperedge HE-6 (induced by source paper "massive unsourced random access based on uncoupled compressive sensing another blessing of massive mimo"):
  with members:
    - "a joint graph based coding scheme for the unsourced random access gaussian channel"
    - "polar coding and random spreading for unsourced multiple access"
    - "massive mimo unsourced random access"
    - "generalized approximate message passing for massive mimo mmwave channel estimation with laplacian prior"
    - "an enhanced decoding algorithm for coded compressed sensing"
    - "sparcs for unsourced random access"
    - "improved scaling law for activity detection in massive mimo systems"
    - "a coupled compressive sensing scheme for unsourced multiple access"

Connected through hyperedge HE-7 (induced by source paper "gaussian approximation of quantization error for estimation from compressed data"):
  with members:
    - "mean estimation from one bit measurements"
    - "learning distributions from their samples under communication constraints"
    - "refined asymptotics for rate distortion using gaussian codebooks for arbitrary sources"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "the distortion rate function of sampled wiener processes"
    - "compress and estimate in multiterminal source coding"
    - "a universal coding scheme for remote generation of continuous random variables"
    - "fundamental distortion limits of analog to digital compression"

Connected through hyperedge HE-8 (induced by source paper "thresholds of descending algorithms in inference problems"):
  with members:
    - "passed spurious descent algorithms and local minima in spiked matrix tensor models"
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "marvels and pitfalls of the langevin algorithm in noisy high dimensional inference"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"

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)