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 "identity connections in residual nets improve noise stability"):
  Abstract: Residual Neural Networks (ResNets) achieve state-of-the-art performance in many computer vision problems. Compared to plain networks without residual connections (PlnNets), ResNets train faster, generalize better, and suffer less from the so-called degradation problem. We introduce simplified (but still nonlinear) versions of ResNets and PlnNets for which these discrepancies still hold, although to a lesser degree. We establish a 1-1 mapping between simplified ResNets and simplified PlnNets, and show that they are exactly equivalent to each other in expressive power for the same computational complexity. We conjecture that ResNets generalize better because they have better noise stability, and empirically support it for both simplified and fully-fledged networks.

Includes members:
  Member "norm preservation why residual networks can become extremely deep":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gresnet graph residual network for reviving deep gnns from suspended animation")
  Member "skip connections eliminate singularities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ap mtl attention pruned multi task learning model for real time instrument detection and segmentation in robot assisted surgery")
  Member "the loss surface of residual networks ensembles and the role of batch normalization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximate fisher information matrix to characterise the training of deep neural networks")
  Member "residual connections encourage iterative inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "toward fast and accurate human pose estimation via soft gated skip connections")
  Member "multi residual networks improving the speed and accuracy of residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "selective kernel networks")
  Member "deep networks with stochastic depth":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on feature normalization and data augmentation")
  Member "residual networks behave like ensembles of relatively shallow networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scaling laws for neural language models")
  Member "highway and residual networks learn unrolled iterative estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "toward fast and accurate human pose estimation via soft gated skip connections")

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)