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 exploding gradient problem demystified definition prevalence impact origin tradeoffs and solutions"):
  Abstract: Whereas it is believed that techniques such as Adam, batch normalization and, more recently, SeLU nonlinearities "solve" the exploding gradient problem, we show that this is not the case in general and that in a range of popular MLP architectures, exploding gradients exist and that they limit the depth to which networks can be effectively trained, both in theory and in practice. We explain why exploding gradients occur and highlight the *collapsing domain problem*, which can arise in architectures that avoid exploding gradients. #R##N#ResNets have significantly lower gradients and thus can circumvent the exploding gradient problem, enabling the effective training of much deeper networks. We show this is a direct consequence of the Pythagorean equation. By noticing that *any neural network is a residual network*, we devise the *residual trick*, which reveals that introducing skip connections simplifies the network mathematically, and that this simplicity may be the major cause for their success.

Includes members:
  Member "replacement autoencoder a privacy preserving algorithm for sensory data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mobile sensor data anonymization")
  Member "training and inference with integers in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sparse weight activation training")
  Member "stable architectures for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a comprehensive and modularized statistical framework for gradient norm equality in deep neural networks")
  Member "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a comprehensive and modularized statistical framework for gradient norm equality in deep neural networks")
  Member "self normalizing neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "soft root sign activation function")
  Member "batch renormalization towards reducing minibatch dependence in batch normalized models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rethinking batch normalization in transformers")
  Member "learning across scales a multiscale method for convolution neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a derivative free method for solving elliptic partial differential equations with deep neural networks")
  Member "the shattered gradients problem if resnets are the answer then what is the question":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "regularizing activations in neural networks via distribution matching with the wasserstein metric")

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)