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: "sgd learns over parameterized networks that provably generalize on linearly separable data"
  Abstract: Neural networks exhibit good generalization behavior in the over-parameterized regime, where the number of network parameters exceeds the number of observations. Nonetheless, current generalization bounds for neural networks fail to explain this phenomenon. In an attempt to bridge this gap, we study the problem of learning a two-layer over-parameterized neural network, when the data is generated by a linearly separable function. In the case where the network has Leaky ReLU activations, we provide both optimization and generalization guarantees for over-parameterized networks. Specifically, we prove convergence rates of SGD to a global minimum and provide generalization guarantees for this global minimum that are independent of the network size. Therefore, our result clearly shows that the use of SGD for optimization both finds a global minimum, and avoids overfitting despite the high capacity of the model. This is the first theoretical demonstration that SGD can avoid overfitting, when learning over-specified neural network classifiers.

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 "norm based generalisation bounds for multi class convolutional neural networks"):
  with members:
    - "efficient and accurate estimation of lipschitz constants for deep neural networks"
    - "size free generalization bounds for convolutional neural networks"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "data dependent pac bayes priors via differential privacy"

Connected through hyperedge HE-3 (induced by source paper "learning parities with neural networks"):
  with members:
    - "quadratic suffices for over parametrization via matrix chernoff bound"
    - "an improved analysis of training over parameterized deep neural networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"
    - "mildly overparametrized neural nets can memorize training data efficiently"
    - "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"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"

Connected through hyperedge HE-4 (induced by source paper "global convergence of deep networks with one wide layer followed by pyramidal topology"):
  with members:
    - "how much over parameterization is sufficient to learn deep relu networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "the effect of network width on stochastic gradient descent and generalization an empirical study"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"
    - "global convergence of adaptive gradient methods for an over parameterized neural network"
    - "on connected sublevel sets in deep learning"
    - "neural networks learning and memorization with almost no over parameterization"
    - "small nonlinearities in activation functions create bad local minima in neural networks"

Connected through hyperedge HE-5 (induced by source paper "global convergence and geometric characterization of slow to fast weight evolution in neural network training for classifying linearly non separable data"):
  with members:
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "on the loss landscape of a class of deep neural networks with no bad local valleys"
    - "understanding the loss surface of neural networks for binary classification"
    - "a convergence theory for deep learning via over parameterization"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-6 (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-7 (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-8 (induced by source paper "revisiting landscape analysis in deep neural networks eliminating decreasing paths to infinity"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "elimination of all bad local minima in deep learning"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "on the connection between learning two layers neural networks and tensor decomposition"
    - "learning one hidden layer neural networks under general input distributions"
    - "over parameterized deep neural networks have no strict local minima for any continuous activations"

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)