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 "coherent gradients an approach to understanding generalization in gradient descent based optimization"):
  Abstract: An open question in the Deep Learning community is why neural networks trained with Gradient Descent generalize well on real datasets even though they are capable of fitting random data. We propose an approach to answering this question based on a hypothesis about the dynamics of gradient descent that we call Coherent Gradients: Gradients from similar examples are similar and so the overall gradient is stronger in certain directions where these reinforce each other. Thus changes to the network parameters during training are biased towards those that (locally) simultaneously benefit many examples when such similarity exists. We support this hypothesis with heuristic arguments and perturbative experiments and outline how this can explain several common empirical observations about Deep Learning. Furthermore, our analysis is not just descriptive, but prescriptive. It suggests a natural modification to gradient descent that can greatly reduce overfitting.

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 "unique properties of wide minima in deep networks")
  Member "uniform convergence may be unable to explain generalization in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dissecting non vacuous generalization bounds based on the mean field approximation")
  Member "stiffness a new perspective on generalization in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interference and generalization in temporal difference learning")
  Member "circuit based intrinsic methods to detect overfitting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "making logic learnable with neural networks")
  Member "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "batch normalization biases deep residual networks towards shallow paths")
  Member "deep learning is robust to massive label noise":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "derivative manipulation for general example weighting")
  Member "the global optimization geometry of shallow linear neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks")
  Member "towards understanding the role of over parametrization in generalization of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the implicit and explicit regularization effects of dropout")

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)