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 nonlinearity coefficient predicting overfitting in deep neural networks"):
  Abstract: For a long time, designing neural architectures that exhibit high performance was considered a dark art that required expert hand-tuning. One of the few well-known guidelines for architecture design is the avoidance of exploding gradients, though even this guideline has remained relatively vague and circumstantial. We introduce the nonlinearity coefficient (NLC), a measurement of the complexity of the function computed by a neural network that is based on the magnitude of the gradient. Via an extensive empirical study, we show that the NLC is a powerful predictor of test error and that attaining a right-sized NLC is essential for optimal performance. #R##N#The NLC exhibits a range of intriguing and important properties. It is closely tied to the amount of information gained from computing a single network gradient. It is tied to the error incurred when replacing the nonlinearity operations in the network with linear operations. It is not susceptible to the confounders of multiplicative scaling, additive bias and layer width. It is stable from layer to layer. Hence, we argue that the NLC is the first robust predictor of overfitting in deep networks.

Includes members:
  Member "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "soft root sign activation function")
  Member "the shattered gradients problem if resnets are the answer then what is the question":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "regularizing activations in neural networks via distribution matching with the wasserstein metric")
  Member "identity mappings in deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "deep vs shallow networks an approximation theory perspective":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "understanding generalization in deep learning via tensor methods")
  Member "deep pyramidal residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on feature normalization and data augmentation")
  Member "the power of depth for feedforward neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "representation benefits of deep feedforward networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neural contextual bandits with ucb based exploration")

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)