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: "width provably matters in optimization for deep linear neural networks"
  Abstract: We prove that for an $L$-layer fully-connected linear neural network, if the width of every hidden layer is $\tilde\Omega (L \cdot r \cdot d_{\mathrm{out}} \cdot \kappa^3 )$, where $r$ and $\kappa$ are the rank and the condition number of the input data, and $d_{\mathrm{out}}$ is the output dimension, then gradient descent with Gaussian random initialization converges to a global minimum at a linear rate. The number of iterations to find an $\epsilon$-suboptimal solution is $O(\kappa \log(\frac{1}{\epsilon}))$. Our polynomial upper bound on the total running time for wide deep linear networks and the $\exp\left(\Omega\left(L\right)\right)$ lower bound for narrow deep linear neural networks [Shamir, 2018] together demonstrate that wide layers are necessary for optimizing deep models.

Connected through hyperedge HE-1 (induced by source paper "backward feature correction how deep learning performs deep learning"):
  with members:
    - "dynamics of deep neural networks and neural tangent hierarchy"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "finite depth and width corrections to the neural tangent kernel"
    - "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"
    - "can sgd learn recurrent neural networks with provable generalization"

Connected through hyperedge HE-2 (induced by source paper "on the global convergence of training deep linear resnets"):
  with members:
    - "provable benefit of orthogonal initialization in optimizing deep linear networks"
    - "an improved analysis of training over parameterized deep neural networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "on learning over parameterized neural networks a functional approximation prospective"
    - "algorithm dependent generalization bounds for overparameterized deep residual networks"
    - "training over parameterized deep resnet is almost as easy as training a two layer network"
    - "on exact computation with an infinitely wide neural net"
    - "global convergence of gradient descent for deep linear residual networks"

Connected through hyperedge HE-3 (induced by source paper "statistical guarantees of generative adversarial networks for distribution estimation"):
  with members:
    - "improving generalization and stability of generative adversarial networks"
    - "efficient approximation of deep relu networks for functions on low dimensional manifolds"
    - "fine grained analysis of optimization and generalization for overparameterized two layer 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"
    - "improving gan training via binarized representation entropy bre regularization"
    - "approximability of discriminators implies diversity in gans"

Connected through hyperedge HE-4 (induced by source paper "provable benefit of orthogonal initialization in optimizing deep linear networks"):
  with members:
    - "dynamical isometry and a mean field theory of lstms and grus"
    - "exponential convergence time of gradient descent for one dimensional deep linear neural networks"
    - "stable architectures for deep neural networks"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "deep linear neural networks with arbitrary loss all local minima are global"
    - "depth creates no bad local minima"
    - "global optimality conditions for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "convex duality of deep neural networks"):
  with members:
    - "implicit regularization in deep matrix factorization"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "exponential convergence time of gradient descent for one dimensional deep linear neural networks"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "deep linear neural networks with arbitrary loss all local minima are global"
    - "in search of the real inductive bias on the role of implicit regularization in deep learning"

Connected through hyperedge HE-6 (induced by source paper "deep gated networks a framework to understand training and generalisation in deep learning"):
  with members:
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "decoupling gating from linearity"
    - "gradient descent finds global minima of deep neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-7 (induced by source paper "quadratic suffices for over parametrization via matrix chernoff bound"):
  with members:
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning two layer rectified neural networks in polynomial time"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "on the convergence rate of training recurrent neural networks"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "a convergence theory for deep learning via over parameterization"

Connected through hyperedge HE-8 (induced by source paper "implicit regularization in deep matrix factorization"):
  with members:
    - "implicit regularization of discrete gradient dynamics in linear neural networks"
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "exploring generalization in deep learning"

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)