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: "coherence analysis of iterative thresholding algorithms"
  Abstract: There is a recent surge of interest in developing algorithms for finding sparse solutions of underdetermined systems of linear equations $y = \Phi x$. In many applications, extremely large problem sizes are envisioned, with at least tens of thousands of equations and hundreds of thousands of unknowns. For such problem sizes, low computational complexity is paramount. The best studied $\ell_1$ minimization algorithm is not fast enough to fulfill this need. Iterative thresholding algorithms have been proposed to address this problem. In this paper we want to analyze two of these algorithms theoretically, and give sufficient conditions under which they recover the sparsest solution.

Connected through hyperedge HE-1 (induced by source paper "optimal k thresholding algorithms for sparse optimization problems"):
  with members:
    - "matrix recipes for hard thresholding methods"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "xsense learning sense separated sparse representations and textual definitions for explainable word sense networks"):
  with members:
    - "learning to compute word embeddings on the fly"
    - "spine sparse interpretable neural embeddings"
    - "word translation without parallel data"
    - "muse modularizing unsupervised sense embeddings"
    - "linear algebraic structure of word senses with applications to polysemy"
    - "the mythos of model interpretability"
    - "skip thought vectors"
    - "learning to understand phrases by embedding the dictionary"

Connected through hyperedge HE-3 (induced by source paper "mission ultra large scale feature selection using count sketches"):
  with members:
    - "on iterative hard thresholding methods for high dimensional m estimation"
    - "efficient estimation of word representations in vector space"
    - "towards ultrahigh dimensional feature selection for big data"
    - "a reliable effective terascale linear learning system"
    - "feature hashing for large scale multitask learning"
    - "sparse online learning via truncated gradient"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "iterative channel estimation using lse and sparse message passing for mmwave mimo systems"):
  with members:
    - "low complexity sparse bayesian learning using combined belief propagation and mean field with a stretched factor graph"
    - "beamspace channel estimation for millimeter wave massive mimo systems with lens antenna array"
    - "a lse and sparse message passing based channel estimation for mmwave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "low rf complexity technologies to enable millimeter wave mimo with large antenna array for 5g wireless communications"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"

Connected through hyperedge HE-5 (induced by source paper "parameterless optimal approximate message passing"):
  with members:
    - "expectation maximization gaussian mixture approximate message passing"
    - "approximate message passing with consistent parameter estimation and applications to sparse learning"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "graphical models concepts in compressed sensing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "k sparse autoencoders"):
  with members:
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "fast inference in sparse coding algorithms with applications to object recognition"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "sparse solution of underdetermined linear equations via adaptively iterative thresholding"):
  with members:
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "orthogonal matching pursuit with replacement"):
  with members:
    - "remarks on the restricted isometry property in orthogonal matching pursuit algorithm"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"
    - "guaranteed rank minimization via singular value projection"
    - "message passing algorithms for compressed sensing"
    - "multi label prediction via compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding 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)