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 on neural networks learns functions of increasing complexity"
  Abstract: We perform an experimental study of the dynamics of Stochastic Gradient Descent (SGD) in learning deep neural networks for several real and synthetic classification tasks. We show that in the initial epochs, almost all of the performance improvement of the classifier obtained by SGD can be explained by a linear classifier. More generally, we give evidence for the hypothesis that, as iterations progress, SGD learns functions of increasing complexity. This hypothesis can be helpful in explaining why SGD-learned classifiers tend to generalize well even in the over-parameterized regime. We also show that the linear classifier learned in the initial stages is "retained" throughout the execution even if training is continued to the point of zero training error, and complement this with a theoretical result in a simplified model. Key to our work is a new measure of how well one classifier explains the performance of another, based on conditional mutual information.

Connected through hyperedge HE-1 (induced by source paper "do we need zero training loss after achieving zero training error"):
  with members:
    - "augmenting data with mixup for sentence classification an empirical study"
    - "deep double descent where bigger models and more data hurt"
    - "pumpout a meta approach to robust deep learning with noisy labels"
    - "mixmatch a holistic approach to semi supervised learning"
    - "deep learning for classical japanese literature"
    - "pumpout a meta approach for robustly training deep neural networks with noisy labels"
    - "positive unlabeled learning with non negative risk estimator"
    - "decoupled weight decay regularization"

Connected through hyperedge HE-2 (induced by source paper "learning not to learn in the presence of noisy labels"):
  with members:
    - "laprop a better way to combine momentum with adaptive gradient"
    - "deep gamblers learning to abstain with portfolio theory"
    - "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"
    - "how does disagreement help generalization against label corruption"
    - "selectivenet a deep neural network with an integrated reject option"
    - "l_dmi an information theoretic noise robust loss function"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "towards understanding the spectral bias of deep learning"):
  with members:
    - "how much over parameterization is sufficient to learn deep relu networks"
    - "an improved analysis of training over parameterized deep neural networks"
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "generalization error bounds of gradient descent for learning over parameterized 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"
    - "on learning over parameterized neural networks a functional approximation prospective"
    - "global convergence of adaptive gradient methods for an over parameterized neural network"

Connected through hyperedge HE-4 (induced by source paper "how does learning rate decay help modern neural networks"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "on the convergence of adam and beyond"
    - "transfusion understanding transfer learning for medical imaging"
    - "do better imagenet models transfer better"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "gradient descent finds global minima of deep neural networks"
    - "understanding batch normalization"

Connected through hyperedge HE-5 (induced by source paper "towards explaining the regularization effect of initial large learning rate in training neural networks"):
  with members:
    - "the step decay schedule a near optimal geometrically decaying learning rate procedure"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "what can resnet learn efficiently going beyond kernels"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "the implicit bias of depth how incremental learning drives generalization"):
  with members:
    - "implicit regularization of discrete gradient dynamics in linear neural networks"
    - "kernel and deep regimes in overparametrized models"
    - "gradient dynamics of shallow univariate relu networks"
    - "the convergence rate of neural networks for learned functions of different frequencies"
    - "implicit regularization in deep matrix factorization"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "on the learning dynamics of deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "deep double descent where bigger models and more data hurt"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "two models of double descent for weak features"
    - "a new look at an old problem a universal learning approach to linear regression"
    - "harmless interpolation of noisy data in regression"
    - "attention is all you need"
    - "understanding deep learning requires rethinking generalization"
    - "identity mappings in deep residual networks"

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)