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: "global optimality in tensor factorization deep learning and beyond"
  Abstract: Techniques involving factorization are found in a wide range of applications and have enjoyed significant empirical success in many fields. However, common to a vast majority of these problems is the significant disadvantage that the associated optimization problems are typically non-convex due to a multilinear form or other convexity destroying transformation. Here we build on ideas from convex relaxations of matrix factorizations and present a very general framework which allows for the analysis of a wide range of non-convex factorization problems - including matrix factorization, tensor factorization, and deep neural network training formulations. We derive sufficient conditions to guarantee that a local minimum of the non-convex optimization problem is a global minimum and show that if the size of the factorized variables is large enough then from any initialization it is possible to find a global minimizer using a purely local descent algorithm. Our framework also provides a partial theoretical justification for the increasingly common use of Rectified Linear Units (ReLUs) in deep neural networks and offers guidance on deep network architectures and regularization strategies to facilitate efficient optimization.

Connected through hyperedge HE-1 (induced by source paper "a generalization theory of gradient descent for learning over parameterized deep relu networks"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "implicit bias of gradient descent on linear convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "generalization error bounds of gradient descent for learning over parameterized deep relu networks"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "an improved analysis of training over parameterized deep neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "tight sample complexity of learning one hidden layer convolutional neural networks"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "learning one hidden layer neural networks under general input distributions"

Connected through hyperedge HE-3 (induced by source paper "how much over parameterization is sufficient to learn deep relu networks"):
  with members:
    - "two models of double descent for weak features"
    - "beyond linearization on quadratic and higher order approximation of wide neural networks"
    - "an improved analysis of training over parameterized deep neural networks"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "over parameterized two level neural networks can learn near optimal feature representations"
    - "convex formulation of overparameterized deep neural networks"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"

Connected through hyperedge HE-4 (induced by source paper "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"):
  with members:
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"

Connected through hyperedge HE-5 (induced by source paper "beyond linearization on quadratic and higher order approximation of wide neural networks"):
  with members:
    - "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"
    - "kernel and deep regimes in overparametrized models"
    - "on nonconvex optimization for machine learning gradients stochasticity and saddle points"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the power and limitations of random features for understanding neural networks"

Connected through hyperedge HE-6 (induced by source paper "the non convex geometry of low rank matrix optimization"):
  with members:
    - "global optimality in low rank matrix optimization"
    - "how to escape saddle points efficiently"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "low rank positive semidefinite matrix recovery from corrupted rank one measurements"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "the global optimization geometry of low rank matrix optimization"
    - "overcomplete tensor decomposition via convex optimization"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"

Connected through hyperedge HE-7 (induced by source paper "stochastic conditional generative networks with basis decomposition"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "diversity sensitive conditional generative adversarial networks"
    - "tafe net task aware feature embeddings for low shot learning"
    - "multi agent diverse generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "texturegan controlling deep image synthesis with texture patches"
    - "mathematics of deep learning"
    - "few shot image recognition by predicting parameters from activations"

Connected through hyperedge HE-8 (induced by source paper "why learning of large scale neural networks behaves like convex optimization"):
  with members:
    - "a new perspective on machine learning how to do perfect supervised learning"
    - "gradient descent finds global minima of deep neural networks"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "a convergence theory for deep learning via over parameterization"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "the loss surface of deep and wide neural networks"
    - "spurious local minima are common in two layer relu neural networks"
    - "qualitatively characterizing neural network optimization problems"

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.NA(Numerical Analysis)