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 "the implicit bias of depth how incremental learning drives generalization"):
  Abstract: A leading hypothesis for the surprising generalization of neural networks is that the dynamics of gradient descent bias the model towards simple solutions, by searching through the solution space in an incremental order of complexity. We formally define the notion of incremental learning dynamics and derive the conditions on depth and initialization for which this phenomenon arises in deep linear models. Our main theoretical contribution is a dynamical depth separation result, proving that while shallow models can exhibit incremental learning dynamics, they require the initialization to be exponentially small for these dynamics to present themselves. However, once the model becomes deeper, the dependence becomes polynomial and incremental learning can arise in more natural settings. We complement our theoretical findings by experimenting with deep matrix sensing, quadratic neural networks and with binary classification using diagonal and convolutional linear networks, showing all of these models exhibit incremental learning.

Includes members:
  Member "implicit regularization of discrete gradient dynamics in linear neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a sample complexity separation between non convex and convex meta learning")
  Member "sgd on neural networks learns functions of increasing complexity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "do we need zero training loss after achieving zero training error")
  Member "kernel and deep regimes in overparametrized models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "gradient dynamics of shallow univariate relu networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "convex geometry and duality of over parameterized neural networks")
  Member "the convergence rate of neural networks for learned functions of different frequencies":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "denoising and regularization via exploiting the structural bias of convolutional generators")
  Member "implicit regularization in deep matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "on the optimization of deep networks implicit acceleration by overparameterization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "disentangling adaptive gradient methods from learning rates")

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)