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: "three factors influencing minima in sgd"
  Abstract: We study the statistical properties of the endpoint of stochastic gradient descent (SGD). We approximate SGD as a stochastic differential equation (SDE) and consider its Boltzmann Gibbs equilibrium distribution under the assumption of isotropic variance in loss gradients.. Through this analysis, we find that three factors – learning rate, batch size and the variance of the loss gradients – control the trade-off between the depth and width of the minima found by SGD, with wider minima favoured by a higher ratio of learning rate to batch size. In the equilibrium distribution only the ratio of learning rate to batch size appears, implying that it’s invariant under a simultaneous rescaling of each by the same amount. We experimentally show how learning rate and batch size affect SGD from two perspectives: the endpoint of SGD and the dynamics that lead up to it. For the endpoint, the experiments suggest the endpoint of SGD is similar under simultaneous rescaling of batch size and learning rate, and also that a higher ratio leads to flatter minima, both findings are consistent with our theoretical analysis. We note experimentally that the dynamics also seem to be similar under the same rescaling of learning rate and batch size, which we explore showing that one can exchange batch size and learning rate in a cyclical learning rate schedule. Next, we illustrate how noise affects memorization, showing that high noise levels lead to better generalization. Finally, we find experimentally that the similarity under simultaneous rescaling of learning rate and batch size breaks down if the learning rate gets too large or the batch size gets too small.

Connected through hyperedge HE-1 (induced by source paper "the implicit and explicit regularization effects of dropout"):
  with members:
    - "on dropout and nuclear norm regularization"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "kernel and deep regimes in overparametrized models"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "transformer xl attentive language models beyond a fixed length context"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "implicit regularization in deep matrix factorization"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"

Connected through hyperedge HE-2 (induced by source paper "sqwa stochastic quantized weight averaging for improving the generalization capability of low precision deep neural networks"):
  with members:
    - "weight normalization based quantization for deep neural network compression"
    - "knowledge distillation for optimization of quantized deep neural networks"
    - "quantization networks"
    - "swalp stochastic weight averaging in low precision training"
    - "empirical analysis of knowledge distillation technique for optimization of quantized deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "an empirical study of large batch stochastic gradient descent with structured covariance noise"):
  with members:
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "understanding short horizon bias in stochastic meta optimization"
    - "a coordinate free construction of scalable natural gradient"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "measuring the effects of data parallelism on neural network training"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "generalization bounds of sgld for non convex learning two theoretical viewpoints"
    - "noisy natural gradient as variational inference"

Connected through hyperedge HE-4 (induced by source paper "wide minima density hypothesis and the explore exploit learning rate schedule"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "time matters in regularizing deep networks weight decay and data augmentation affect early learning dynamics matter little near convergence"
    - "towards understanding generalization in gradient based meta learning"
    - "an empirical model of large batch training"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "identifying generalization properties in neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "stochasticity of deterministic gradient descent large learning rate for multiscale objective function"):
  with members:
    - "variational optimization on lie groups with examples of leading generalized eigenvalue problems"
    - "analysis of momentum methods"
    - "towards explaining the regularization effect of initial large learning rate in training 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"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"

Connected through hyperedge HE-6 (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"
    - "gradient descent aligns the layers of deep linear networks"
    - "step size matters in deep learning"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"

Connected through hyperedge HE-7 (induced by source paper "hyperplane arrangements of trained convnets are biased"):
  with members:
    - "geometry of deep convolutional networks"
    - "an empirical study of example forgetting during deep neural network learning"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "bounding and counting linear regions of deep neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "understanding deep neural networks with rectified linear units"

Connected through hyperedge HE-8 (induced by source paper "feature robustness flatness and generalization error for deep neural networks"):
  with members:
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "identifying generalization properties in neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "exploring generalization in deep learning"
    - "fisher rao metric geometry and complexity of neural networks"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "theory of deep learning iib optimization properties of sgd"
    - "sharp minima can generalize for deep nets"

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)