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: "the effect of network width on stochastic gradient descent and generalization an empirical study"
  Abstract: We investigate how the final parameters found by stochastic gradient descent are influenced by over-parameterization. We generate families of models by increasing the number of channels in a base network, and then perform a large hyper-parameter search to study how the test error depends on learning rate, batch size, and network width. We find that the optimal SGD hyper-parameters are determined by a "normalized noise scale," which is a function of the batch size, learning rate, and initialization conditions. In the absence of batch normalization, the optimal normalized noise scale is directly proportional to width. Wider networks, with their higher optimal noise scale, also achieve higher test accuracy. These observations hold for MLPs, ConvNets, and ResNets, and for two different parameterization schemes ("Standard" and "NTK"). We observe a similar trend with batch normalization for ResNets. Surprisingly, since the largest stable learning rate is bounded, the largest batch size consistent with the optimal normalized noise scale decreases as the width increases.

Connected through hyperedge HE-1 (induced by source paper "batch normalization biases deep residual networks towards shallow paths"):
  with members:
    - "provable benefit of orthogonal initialization in optimizing deep linear networks"
    - "freeze and chaos for dnns an ntk view of batch normalization checkerboard and boundary effects"
    - "filter response normalization layer eliminating batch dependence in the training of deep neural networks"
    - "a mean field theory of batch normalization"
    - "fixup initialization residual learning without normalization"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent"
    - "self training with noisy student improves imagenet classification"

Connected through hyperedge HE-2 (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"
    - "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"
    - "over parameterized deep neural networks have no strict local minima for any continuous activations"

Connected through hyperedge HE-3 (induced by source paper "on the infinite width limit of neural networks with a standard parameterization"):
  with members:
    - "freeze and chaos for dnns an ntk view of batch normalization checkerboard and boundary effects"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "asymptotics of wide networks from feynman diagrams"
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "on exact computation with an infinitely wide neural net"
    - "information in infinite ensembles of infinitely wide neural networks"
    - "graph neural tangent kernel fusing graph neural networks with graph kernels"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-4 (induced by source paper "harnessing the power of infinitely wide deep nets on small data tasks"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "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"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "on the bias variance tradeoff textbooks need an update"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "deep double descent where bigger models and more data hurt"
    - "measuring the intrinsic dimension of objective landscapes"
    - "three factors influencing minima in sgd"
    - "gradient descent happens in a tiny subspace"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "an analysis of deep neural network models for practical applications"

Connected through hyperedge HE-6 (induced by source paper "on the intrinsic privacy of stochastic gradient descent"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "evaluating differentially private machine learning in practice"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "deep reinforcement learning that matters"
    - "differentially private federated learning a client level perspective"
    - "data dependent stability of stochastic gradient descent"
    - "sharp minima can generalize for deep nets"
    - "the loss surface of deep and wide neural networks"

Connected through hyperedge HE-7 (induced by source paper "disentangling feature and lazy learning in deep neural networks an empirical study"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "asymptotics of wide networks from feynman diagrams"
    - "the asymptotic spectrum of the hessian of dnn throughout training"
    - "mean field limit of the learning dynamics of multilayer neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "a modern take on the bias variance tradeoff in neural networks"

Connected through hyperedge HE-8 (induced by source paper "disentangling feature and lazy training in deep neural networks"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "asymptotics of wide networks from feynman diagrams"
    - "the asymptotic spectrum of the hessian of dnn throughout training"
    - "mean field limit of the learning dynamics of multilayer neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "a modern take on the bias variance tradeoff in neural networks"
    - "a convergence theory for deep learning via over parameterization"

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)