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 "unique properties of wide minima in deep networks"):
  Abstract: It is well known that (stochastic) gradient descent has an implicit bias towards wide minima. In deep neural network training, this mechanism serves to screen out minima. However, the precise effect that this has on the trained network is not yet fully understood. In this paper, we characterize the wide minima in linear neural networks trained with a quadratic loss. First, we show that linear ResNets with zero initialization necessarily converge to the widest of all minima. We then prove that these minima correspond to nearly balanced networks whereby the gain from the input to any intermediate representation does not change drastically from one layer to the next. Finally, we show that consecutive layers in wide minima solutions are coupled. That is, one of the left singular vectors of each weight matrix, equals one of the right singular vectors of the next matrix. This forms a distinct path from input to output, that, as we show, is dedicated to the signal that experiences the largest gain end-to-end. Experiments indicate that these properties are characteristic of both linear and nonlinear models trained in practice.

Includes members:
  Member "a tail index analysis of stochastic gradient noise in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a diffusion theory for deep learning dynamics stochastic gradient descent escapes from sharp minima exponentially fast")
  Member "implicit regularization of normalization methods":
    also connected through other hyperedges:
      (none)
  Member "gradient descent maximizes the margin of homogeneous neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mean field analysis of two layer neural networks non asymptotic rates and generalization bounds")
  Member "on the optimization of deep networks implicit acceleration by overparameterization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  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 "gradient descent aligns the layers of deep linear networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the global convergence of training deep linear resnets")
  Member "step size matters in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "at stability s edge how to adjust hyperparameters to preserve minima selection in asynchronous training of neural networks")
  Member "algorithmic regularization in learning deep homogeneous models layers are automatically balanced":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the global convergence of training deep linear resnets")

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)