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: "sparsity constrained nonlinear optimization optimality conditions and algorithms"
  Abstract: This paper treats the problem of minimizing a general continuously differentiable function subject to sparsity constraints. We present and analyze several different optimality criteria which are based on the notions of stationarity and coordinate-wise optimality. These conditions are then used to derive three numerical algorithms aimed at finding points satisfying the resulting optimality criteria: the iterative hard thresholding method and the greedy and partial sparse-simplex methods. The first algorithm is essentially a gradient projection method while the remaining two algorithms are of coordinate descent type. The theoretical convergence of these methods and their relations to the derived optimality conditions are studied. The algorithms and results are illustrated by several numerical examples.

Connected through hyperedge HE-1 (induced by source paper "efficient relaxed gradient support pursuit for sparsity constrained non convex optimization"):
  with members:
    - "a simple stochastic variance reduced algorithm with fast convergence rates"
    - "tractable and scalable schatten quasi norm approximations for rank minimization"
    - "direct acceleration of saga using sampled negative momentum"
    - "asvrg accelerated proximal svrg"
    - "fast stochastic variance reduced gradient method with momentum acceleration for machine learning"
    - "scalable algorithms for tractable schatten quasi norm minimization"
    - "stochastic variance reduced optimization for nonconvex sparse learning"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"

Connected through hyperedge HE-2 (induced by source paper "optimal k thresholding algorithms for sparse optimization problems"):
  with members:
    - "matrix recipes for hard thresholding methods"
    - "coherence analysis of iterative thresholding algorithms"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "functional nonlinear sparse models"):
  with members:
    - "learning power spectrum maps from quantized power measurements"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "block iterative reweighted algorithms for super resolution of spectrally sparse signals"
    - "forward backward greedy algorithms for atomic norm regularization"
    - "computational complexity of certifying restricted isometry property"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "atomic norm denoising with applications to line spectral estimation"

Connected through hyperedge HE-4 (induced by source paper "instance optimal decoding and the restricted isometry property"):
  with members:
    - "sketching for large scale learning of mixture models"
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "small width low distortions quasi isometric embeddings with quantized sub gaussian random projections"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "representation and coding of signal geometry"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "on dc based methods for phase retrieval"):
  with members:
    - "phase retrieval from the norms of affine transformations"
    - "generalized phase retrieval measurement number matrix recovery and beyond"
    - "a geometric analysis of phase retrieval"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval an overview of recent developments"
    - "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems"
    - "phase retrieval stability and recovery guarantees"

Connected through hyperedge HE-6 (induced by source paper "a decomposition algorithm for sparse generalized eigenvalue problem"):
  with members:
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "efficient and non convex coordinate descent for symmetric nonnegative matrix factorization"
    - "incremental training of a detector using online sparse eigendecomposition"
    - "efficiently learning a detection cascade with sparse eigenvectors"
    - "optimal solutions for sparse principal component analysis"
    - "a direct formulation for sparse pca using semidefinite programming"

Connected through hyperedge HE-7 (induced by source paper "sparse wide area control of power systems using data driven reinforcement learning"):
  with members:
    - "game theoretic multi agent control and network cost allocation under communication constraints"
    - "integral concurrent learning adaptive control with parameter convergence without pe or state derivatives"

Connected through hyperedge HE-8 (induced by source paper "fourier phase retrieval uniqueness and algorithms"):
  with members:
    - "message passing algorithms for synchronization problems over compact groups"
    - "bispectrum inversion with application to multireference alignment"
    - "on fienup methods for sparse phase retrieval"
    - "on signal reconstruction from frog measurements"
    - "on the 2d phase retrieval problem"
    - "multiple illumination phaseless super resolution mips with applications to phaseless doa estimation and diffraction imaging"
    - "on the uniqueness of frog methods"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"

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)