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 happens in a tiny subspace"
  Abstract: We show that in a variety of large-scale deep learning scenarios the gradient dynamically converges to a very small subspace after a short period of training. The subspace is spanned by a few top eigenvectors of the Hessian (equal to the number of classes in the dataset), and is mostly preserved over long periods of training. A simple argument then suggests that gradient descent may happen mostly in this subspace. We give an example of this effect in a solvable model of classification, and we comment on possible implications for optimization and learning.

Connected through hyperedge HE-1 (induced by source paper "scaling laws for neural language models"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"
    - "growing a brain fine tuning by increasing model capacity"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "autogrow automatic layer growing in deep convolutional networks"
    - "a constructive prediction of the generalization error across scales"
    - "universal transformers"
    - "generating long sequences with sparse transformers"

Connected through hyperedge HE-2 (induced by source paper "the early phase of neural network training"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "stabilizing the lottery ticket hypothesis"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "time matters in regularizing deep networks weight decay and data augmentation affect early learning dynamics matter little near convergence"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "unsupervised representation learning by predicting image rotations"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-3 (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-4 (induced by source paper "hessian based analysis of sgd for deep nets dynamics and generalization"):
  with members:
    - "escaping saddle points with adaptive gradient methods"
    - "measurements of three level hierarchical structure in the outliers in the spectrum of deepnet hessians"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "on the convergence of adam and beyond"
    - "positively scale invariant flatness of relu neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "small nonlinearities in activation functions create bad local minima in neural networks"

Connected through hyperedge HE-5 (induced by source paper "a simple baseline for bayesian uncertainty in deep learning"):
  with members:
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "swalp stochastic weight averaging in low precision training"
    - "subspace inference for bayesian deep learning"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "averaging weights leads to wider optima and better generalization"
    - "fixing variational bayes deterministic variational inference for bayesian neural networks"

Connected through hyperedge HE-6 (induced by source paper "linear mode connectivity and the lottery ticket hypothesis"):
  with members:
    - "the early phase of neural network training"
    - "picking winning tickets before training by preserving gradient flow"
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "an exponential learning rate schedule for deep learning"
    - "what s hidden in a randomly weighted neural network"
    - "the state of sparsity in deep neural networks"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-7 (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"
    - "the effect of network width on stochastic gradient descent and generalization an empirical study"
    - "measuring the intrinsic dimension of objective landscapes"
    - "three factors influencing minima in sgd"
    - "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-8 (induced by source paper "an investigation into neural net optimization via hessian eigenvalue density"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "the full spectrum of deep net hessians at scale dynamics with sample size"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "three factors influencing minima in sgd"
    - "eigenvalues of the hessian in deep learning singularity and beyond"
    - "towards understanding generalization of deep learning perspective of loss landscapes"
    - "visualizing the loss landscape of neural nets"
    - "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)