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: "frequency principle fourier analysis sheds light on deep neural networks"
  Abstract: We study the training process of Deep Neural Networks (DNNs) from the Fourier analysis perspective. We demonstrate a very universal Frequency Principle (F-Principle) --- DNNs often fit target functions from low to high frequencies --- on high-dimensional benchmark datasets such as MNIST/CIFAR10 and deep neural networks such as VGG16. This F-Principle of DNNs is opposite to the behavior of most conventional iterative numerical schemes (e.g., Jacobi method), which exhibit faster convergence for higher frequencies for various scientific computing problems. With a simple theory, we illustrate that this F-Principle results from the regularity of the commonly used activation functions. The F-Principle implies an implicit bias that DNNs tend to fit training data by a low-frequency function. This understanding provides an explanation of good generalization of DNNs on most real datasets and bad generalization of DNNs on parity function or randomized dataset.

Connected through hyperedge HE-1 (induced by source paper "frequency bias in neural networks for input of non uniform density"):
  with members:
    - "dynamics of deep neural networks and neural tangent hierarchy"
    - "towards understanding the spectral bias of deep learning"
    - "kernel and deep regimes in overparametrized models"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the convergence rate of training recurrent neural networks"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "eigenvalue decay implies polynomial time learnability for neural networks"

Connected through hyperedge HE-2 (induced by source paper "towards understanding the spectral bias of deep learning"):
  with members:
    - "sgd on neural networks learns functions of increasing complexity"
    - "how much over parameterization is sufficient to learn deep relu networks"
    - "an improved analysis of training over parameterized 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-3 (induced by source paper "drawing early bird tickets towards more efficient training of deep networks"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "e2 train training state of the art cnns with over 80 energy savings"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "swalp stochastic weight averaging in low precision training"
    - "training high performance and large scale deep neural networks with full 8 bit integers"
    - "energy and policy considerations for deep learning in nlp"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "scalable methods for 8 bit training of neural networks"

Connected through hyperedge HE-4 (induced by source paper "a fine grained spectral perspective on neural networks"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "explicitizing an implicit bias of the frequency principle in two layer neural networks"
    - "a mean field theory of batch normalization"
    - "towards characterizing divergence in deep q learning"
    - "the convergence rate of neural networks for learned functions of different frequencies"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "training behavior of deep neural network in frequency domain"

Connected through hyperedge HE-5 (induced by source paper "explicitizing an implicit bias of the frequency principle in two layer neural networks"):
  with members:
    - "a type of generalization error induced by initialization in deep neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
    - "analysis of the gradient descent algorithm for a deep neural network model with skip connections"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "training behavior of deep neural network in frequency domain"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "frequency principle in deep learning with general loss functions and its potential application"

Connected through hyperedge HE-6 (induced by source paper "the convergence rate of neural networks for learned functions of different frequencies"):
  with members:
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "on the convergence rate of training recurrent neural networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "understanding training and generalization in deep learning by fourier analysis"

Connected through hyperedge HE-7 (induced by source paper "a type of generalization error induced by initialization in deep neural networks"):
  with members:
    - "theory iii dynamics and generalization in deep networks a simple solution"
    - "meta learners learning dynamics are unlike learners"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
    - "analysis of the gradient descent algorithm for a deep neural network model with skip connections"
    - "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "training behavior of deep neural network in frequency domain"

Connected through hyperedge HE-8 (induced by source paper "a priori generalization error for two layer relu neural network through minimum norm solution"):
  with members:
    - "a type of generalization error induced by initialization in deep neural networks"
    - "explicitizing an implicit bias of the frequency principle in two layer neural networks"
    - "multi scale deep neural networks for solving high dimensional pdes"
    - "theory of the frequency principle for general deep neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "a phase shift deep neural network for high frequency approximation and wave problems"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"

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)