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: "gradient descent aligns the layers of deep linear networks"
  Abstract: This paper establishes risk convergence and asymptotic weight matrix alignment --- a form of implicit regularization --- of gradient flow and gradient descent when applied to deep linear networks on linearly separable data. In more detail, for gradient flow applied to strictly decreasing loss functions (with similar results for gradient descent with particular decreasing step sizes): (i) the risk converges to 0; (ii) the normalized i-th weight matrix asymptotically equals its rank-1 approximation $u_iv_i^{\top}$; (iii) these rank-1 matrices are aligned across layers, meaning $|v_{i+1}^{\top}u_i|\to1$. In the case of the logistic loss (binary cross entropy), more can be said: the linear function induced by the network --- the product of its weight matrices --- converges to the same direction as the maximum margin solution. This last property was identified in prior work, but only under assumptions on gradient descent which here are implied by the alignment phenomenon.

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 "unique properties of wide minima in deep networks"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "implicit regularization of normalization methods"
    - "gradient descent maximizes the margin of homogeneous neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "step size matters in deep learning"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"
    - "three factors influencing minima in sgd"

Connected through hyperedge HE-3 (induced by source paper "balancedness and alignment are unlikely in linear neural networks"):
  with members:
    - "how do infinite width bounded norm networks look in function space"
    - "memorization in overparameterized autoencoders"
    - "global convergence of adaptive gradient methods for an over parameterized neural network"
    - "implicit regularization in deep matrix factorization"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"

Connected through hyperedge HE-4 (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"
    - "exponential convergence time of gradient descent for one dimensional deep linear neural networks"
    - "implicit bias of gradient descent on linear convolutional 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-5 (induced by source paper "deep learning theory review an optimal control and dynamical systems perspective"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "a mean field theory of batch normalization"
    - "where is the information in a deep neural network"
    - "dynamical isometry and a mean field theory of lstms and grus"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "neural similarity learning"):
  with members:
    - "compressive hyperspherical energy minimization"
    - "implicit regularization in deep matrix factorization"
    - "a closer look at few shot classification"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "decoupled networks"
    - "meta learning with latent embedding optimization"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "towards understanding the role of over parametrization in generalization of neural networks"

Connected through hyperedge HE-7 (induced by source paper "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"):
  with members:
    - "generalization guarantees for neural networks via harnessing the low rank structure of the jacobian"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "high dimensional robust sparse regression"
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "deep learning is robust to massive label noise"
    - "learning with bad training data via iterative trimmed loss minimization"

Connected through hyperedge HE-8 (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"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "on tighter generalization bound for deep neural networks cnns resnets and beyond"

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)