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: "theory of the frequency principle for general deep neural networks"
  Abstract: Along with fruitful applications of Deep Neural Networks (DNNs) to realistic problems, recently, some empirical studies of DNNs reported a universal phenomenon of Frequency Principle (F-Principle): a DNN tends to learn a target function from low to high frequencies during the training. The F-Principle has been very useful in providing both qualitative and quantitative understandings of DNNs. In this paper, we rigorously investigate the F-Principle for the training dynamics of a general DNN at three stages: initial stage, intermediate stage, and final stage. For each stage, a theorem is provided in terms of proper quantities characterizing the F-Principle. Our results are general in the sense that they work for multilayer networks with general activation functions, population densities of data, and a large class of loss functions. Our work lays a theoretical foundation of the F-Principle for a better understanding of the training process of DNNs.

Connected through hyperedge HE-1 (induced by source paper "spectrum dependent learning curves in kernel regression and wide neural networks"):
  with members:
    - "learning curves for deep neural networks a gaussian field theory perspective"
    - "towards understanding the spectral bias of deep learning"
    - "on exact computation with an infinitely wide neural net"

Connected through hyperedge HE-2 (induced by source paper "frequency principle fourier analysis sheds light on deep neural networks"):
  with members:
    - "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"
    - "understanding training and generalization in deep learning by fourier analysis"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "towards understanding generalization of deep learning perspective of loss landscapes"
    - "failures of gradient based deep learning"

Connected through hyperedge HE-3 (induced by source paper "a priori generalization error for two layer relu neural network through minimum norm solution"):
  with members:
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "multi scale deep neural networks for solving high dimensional pdes"):
  with members:
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "explicitizing an implicit bias of the frequency principle in two layer neural networks"
    - "a phase shift deep neural network for high frequency approximation and wave problems"
    - "training behavior of deep neural network in frequency domain"
    - "understanding training and generalization in deep learning by fourier analysis"
    - "deep learning approximation for stochastic control problems"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "a phase shift deep neural network for high frequency approximation and wave problems"):
  with members:
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "understanding training and generalization in deep learning by fourier analysis"

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)