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: "exponential convergence time of gradient descent for one dimensional deep linear neural networks"
  Abstract: We study the dynamics of gradient descent on objective functions of the form $f(\prod_{i=1}^{k} w_i)$ (with respect to scalar parameters $w_1,\ldots,w_k$), which arise in the context of training depth-$k$ linear neural networks. We prove that for standard random initializations, and under mild assumptions on $f$, the number of iterations required for convergence scales exponentially with the depth $k$. We also show empirically that this phenomenon can occur in higher dimensions, where each $w_i$ is a matrix. This highlights a potential obstacle in understanding the convergence of gradient-based methods for deep linear neural networks, where $k$ is large.

Connected through hyperedge HE-1 (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"
    - "width provably matters in optimization for deep linear 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"

Connected through hyperedge HE-2 (induced by source paper "provable benefit of orthogonal initialization in optimizing deep linear networks"):
  with members:
    - "width provably matters in optimization for deep linear neural networks"
    - "dynamical isometry and a mean field theory of lstms and grus"
    - "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-3 (induced by source paper "convex duality of deep neural networks"):
  with members:
    - "width provably matters in optimization for deep linear neural networks"
    - "implicit regularization in deep matrix factorization"
    - "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"
    - "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-4 (induced by source paper "hessian based analysis of sgd for deep nets dynamics and generalization"):
  with members:
    - "escaping saddle points with adaptive gradient methods"
    - "measurements of three level hierarchical structure in the outliers in the spectrum of deepnet hessians"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "on the convergence of adam and beyond"
    - "positively scale invariant flatness of relu neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "small nonlinearities in activation functions create bad local minima in neural networks"

Connected through hyperedge HE-5 (induced by source paper "width provably matters in optimization for deep linear neural networks"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "gradient descent aligns the layers of deep linear networks"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "a convergence analysis of gradient descent for deep linear neural networks"

Connected through hyperedge HE-6 (induced by source paper "global convergence of gradient descent for deep linear residual networks"):
  with members:
    - "width provably matters in optimization for deep linear neural networks"
    - "fixup initialization residual learning without normalization"
    - "analysis of the gradient descent algorithm for a deep neural network model with skip connections"
    - "gradient descent finds global minima of deep neural networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "a convergence theory for deep learning via over parameterization"

Connected through hyperedge HE-7 (induced by source paper "channel normalization in convolutional neural network avoids vanishing gradients"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "understanding batch normalization"
    - "deep image prior"
    - "instance normalization the missing ingredient for fast stylization"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "a convergence analysis of gradient descent for deep linear neural networks"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "deep linear neural networks with arbitrary loss all local minima are global"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "the loss surface of deep and wide neural networks"
    - "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis"
    - "sgd learns over parameterized networks that provably generalize on linearly separable data"

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)