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: "convergence analysis of proximal gradient with momentum for nonconvex optimization"
  Abstract: In many modern machine learning applications, structures of underlying mathematical models often yield nonconvex optimization problems. Due to the intractability of nonconvexity, there is a rising need to develop efficient methods for solving general nonconvex problems with certain performance guarantee. In this work, we investigate the accelerated proximal gradient method for nonconvex programming (APGnc). The method compares between a usual proximal gradient step and a linear extrapolation step, and accepts the one that has a lower function value to achieve a monotonic decrease. In specific, under a general nonsmooth and nonconvex setting, we provide a rigorous argument to show that the limit points of the sequence generated by APGnc are critical points of the objective function. Then, by exploiting the Kurdyka-{\L}ojasiewicz (\KL) property for a broad class of functions, we establish the linear and sub-linear convergence rates of the function value sequence generated by APGnc. We further propose a stochastic variance reduced APGnc (SVRG-APGnc), and establish its linear convergence under a special case of the \KL property. We also extend the analysis to the inexact version of these methods and develop an adaptive momentum strategy that improves the numerical performance.

Connected through hyperedge HE-1 (induced by source paper "deam adaptive momentum with discriminative weight for stochastic optimization"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "graph neural networks for small graph and giant network representation learning an overview"
    - "normalized direction preserving adam"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "improving generalization performance by switching from adam to sgd"
    - "an overview of gradient descent optimization algorithms"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"

Connected through hyperedge HE-2 (induced by source paper "investigating task driven latent feasibility for nonconvex image modeling"):
  with members:
    - "fastderain a novel video rain streak removal method using directional gradient priors"
    - "plug and play methods provably converge with properly trained denoisers"
    - "deep plug and play super resolution for arbitrary blur kernels"
    - "regularization by denoising clarifications and new interpretations"
    - "on the convergence of learning based iterative methods for nonconvex inverse problems"
    - "acceleration of red via vector extrapolation"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"

Connected through hyperedge HE-3 (induced by source paper "tensor p shrinkage nuclear norm for low rank tensor completion"):
  with members:
    - "low rank tensor completion for multiway visual data"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "low rank tensor completion a riemannian manifold preconditioning approach"
    - "smooth parafac decomposition for tensor completion"
    - "exact tensor completion using t svd"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "nuclear norm of higher order tensors"

Connected through hyperedge HE-4 (induced by source paper "sgd converges to global minimum in deep learning via star convex path"):
  with members:
    - "on the convergence of adam and beyond"
    - "an alternative view when does sgd escape local minima"
    - "a generic approach for escaping saddle points"
    - "how to escape saddle points efficiently"
    - "understanding deep learning requires rethinking generalization"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "on the convergence of learning based iterative methods for nonconvex inverse problems"):
  with members:
    - "deep image prior"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning deep cnn denoiser prior for image restoration"
    - "unrolled optimization with deep priors"
    - "admm net a deep learning approach for compressive sensing mri"
    - "inexact proximal gradient methods for non convex and non smooth optimization"
    - "learning to optimize"
    - "plug and play admm for image restoration fixed point convergence and applications"

Connected through hyperedge HE-6 (induced by source paper "faster fast tensor completion with nonconvex regularization"):
  with members:
    - "large scale low rank matrix learning with nonconvex regularizers"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "a dual framework for low rank tensor completion"
    - "low rank tensor recovery via iterative hard thresholding"
    - "low rank tensor completion a riemannian manifold preconditioning approach"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "parallel matrix factorization for low rank tensor completion"
    - "exact tensor completion using t svd"

Connected through hyperedge HE-7 (induced by source paper "learning collaborative generation correction modules for blind image deblurring and beyond"):
  with members:
    - "toward designing convergent deep operator splitting methods for task specific nonconvex optimization"
    - "proximal alternating direction network a globally converged deep unrolling framework"
    - "learning deep cnn denoiser prior for image restoration"
    - "learning fully convolutional networks for iterative non blind deconvolution"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "cascades of regression tree fields for image restoration"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-8 (induced by source paper "stochastic nested variance reduction for nonconvex optimization"):
  with members:
    - "katyusha x practical momentum method for stochastic sum of nonconvex optimization"
    - "sdca without duality"

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.LG(Machine Learning)