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 "on the learning dynamics of two layer nonlinear convolutional neural networks"):
  Abstract: Convolutional neural networks (CNNs) have achieved remarkable performance in various fields, particularly in the domain of computer vision. However, why this architecture works well remains to be a mystery. In this work we move a small step toward understanding the success of CNNs by investigating the learning dynamics of a two-layer nonlinear convolutional neural network over some specific data distributions. Rather than the typical Gaussian assumption for input data distribution, we consider a more realistic setting that each data point (e.g. image) contains a specific pattern determining its class label. Within this setting, we both theoretically and empirically show that some convolutional filters will learn the key patterns in data and the norm of these filters will dominate during the training process with stochastic gradient descent. And with any high probability, when the number of iterations is sufficiently large, the CNN model could obtain 100% accuracy over the considered data distributions. Our experiments demonstrate that for practical image classification tasks our findings still hold to some extent.

Includes members:
  Member "fine grained analysis of optimization and generalization for overparameterized two layer neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "gradient descent finds global minima of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "learning overparameterized neural networks via stochastic gradient descent on structured data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "implicit bias of gradient descent on linear convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "learning one convolutional layer with overlapping patches":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a deep conditioning treatment of neural networks")
  Member "towards understanding the generalization bias of two layer convolutional linear classifiers with gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sgd on neural networks learns functions of increasing complexity")
  Member "stochastic gradient descent optimizes over parameterized deep relu networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "a convergence theory for deep learning via over parameterization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "backward feature correction how deep learning performs deep learning")

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)