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: "theoretical properties of the global optimizer of two layer neural network"
  Abstract: In this paper, we study the problem of optimizing a two-layer artificial neural network that best fits a training dataset. We look at this problem in the setting where the number of parameters is greater than the number of sampled points. We show that for a wide class of differentiable activation functions (this class involves most nonlinear functions and excludes piecewise linear functions), we have that arbitrary first-order optimal solutions satisfy global optimality provided the hidden layer is non-singular. We essentially show that these non-singular hidden layer matrix satisfy a ``"good" property for these big class of activation functions. Techniques involved in proving this result inspire us to look at a new algorithmic, where in between two gradient step of hidden layer, we add a stochastic gradient descent (SGD) step of the output layer. In this new algorithmic framework, we extend our earlier result and show that for all finite iterations the hidden layer satisfies the``good" property mentioned earlier therefore partially explaining success of noisy gradient methods and addressing the issue of data independency of our earlier result. Both of these results are easily extended to hidden layers given by a flat matrix from that of a square matrix. Results are applicable even if network has more than one hidden layer provided all inner hidden layers are arbitrary, satisfy non-singularity, all activations are from the given class of differentiable functions and optimization is only with respect to the outermost hidden layer. Separately, we also study the smoothness properties of the objective function and show that it is actually Lipschitz smooth, i.e., its gradients do not change sharply. We use smoothness properties to guarantee asymptotic convergence of $O(1/\text{number of iterations})$ to a first-order optimal solution.

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"
    - "width provably matters in optimization for deep linear 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"

Connected through hyperedge HE-2 (induced by source paper "what can resnet learn efficiently going beyond kernels"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "can sgd learn recurrent neural networks with provable generalization"):
  with members:
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "on the convergence rate of training recurrent neural networks"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "recent advances in recurrent neural networks"
    - "a convergence theory for deep learning via over parameterization"
    - "on generalization bounds of a family of recurrent neural networks"
    - "learning one hidden layer neural networks with landscape design"

Connected through hyperedge HE-4 (induced by source paper "convex formulation of overparameterized deep neural networks"):
  with members:
    - "over parameterized two level neural networks can learn near optimal feature representations"
    - "on learning over parameterized neural networks a functional approximation prospective"
    - "mean field limit of the learning dynamics of multilayer neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-5 (induced by source paper "dtn a learning rate scheme with convergence rate of mathcal o 1 t for sgd"):
  with members:
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "convergence analysis of two layer neural networks with relu activation"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "adam a method for stochastic optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-6 (induced by source paper "learning and generalization in overparameterized neural networks going beyond two layers"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "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"
    - "what can resnet learn efficiently going beyond kernels"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "on the benefit of width for neural networks disappearance of bad basins"):
  with members:
    - "mean field limit of the learning dynamics of multilayer neural networks"
    - "on the expressive power of deep polynomial neural networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "roberta a robustly optimized bert pretraining approach"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-8 (induced by source paper "over parameterized deep neural networks have no strict local minima for any continuous activations"):
  with members:
    - "autoslim towards one shot architecture search for channel numbers"
    - "roberta a robustly optimized bert pretraining approach"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "on the loss landscape of a class of deep neural networks with no bad local valleys"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "are resnets provably better than linear predictors"

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)