human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "training behavior of deep neural network in frequency domain"):
  Abstract: Why deep neural networks (DNNs) capable of overfitting often generalize well in practice is a mystery in deep learning. Existing works indicate that this observation holds for both complicated real datasets and simple datasets of one-dimensional (1-d) functions. In this work, for natural images and low-frequency dominant 1-d functions, we empirically found that a DNN with common settings first quickly captures the dominant low-frequency components, and then relatively slowly captures high-frequency ones. We call this phenomenon Frequency Principle (F-Principle). F-Principle can be observed over various DNN setups of different activation functions, layer structures and training algorithms in our experiments. F-Principle can be used to understand (i) the behavior of DNN training in the information plane and (ii) why DNNs often generalize well albeit its ability of overfitting. This F-Principle potentially can provide insights into understanding the general principle underlying DNN optimization and generalization for real datasets.

Includes members:
  Member "frequency principle fourier analysis sheds light on deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "frequency bias in neural networks for input of non uniform density")
  Member "meta learners learning dynamics are unlike learners":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "badger learning to learn learning algorithms through multi agent communication")
  Member "frequency principle in deep learning with general loss functions and its potential application":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "explicitizing an implicit bias of the frequency principle in two layer neural networks")
  Member "understanding training and generalization in deep learning by fourier analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "explaining knowledge distillation by quantifying the knowledge")
  Member "nonlinear collaborative scheme for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "theory of the frequency principle for general deep neural networks")
  Member "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "predicting trends in the quality of state of the art neural networks without access to training or testing data")
  Member "understanding deep learning generalization by maximum entropy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "understanding autoencoders with information theoretic concepts")
  Member "towards understanding generalization of deep learning perspective of loss landscapes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a diffusion theory for deep learning dynamics stochastic gradient descent escapes from sharp minima exponentially fast")

Question: Please tell me which class the source paper 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)