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 "rezero is all you need fast convergence at large depth"):
  Abstract: Deep networks have enabled significant performance gains across domains, but they often suffer from vanishing/exploding gradients. This is especially true for Transformer architectures where depth beyond 12 layers is difficult to train without large datasets and computational budgets. In general, we find that inefficient signal propagation impedes learning in deep networks. In Transformers, multi-head self-attention is the main cause of this poor signal propagation. To facilitate deep signal propagation, we propose ReZero, a simple change to the architecture that initializes an arbitrary layer as the identity map, using a single additional learned parameter per layer. We apply this technique to language modeling and find that we can easily train ReZero-Transformer networks over a hundred layers. When applied to 12 layer Transformers, ReZero converges 56% faster on enwiki8. ReZero applies beyond Transformers to other residual networks, enabling 1,500% faster convergence for deep fully connected networks and 32% faster convergence for a ResNet-56 trained on CIFAR 10.

Includes members:
  Member "batch normalization biases deep residual networks towards shallow paths":
    also connected through other hyperedges:
      (none)
  Member "on layer normalization in the transformer architecture":
    also connected through other hyperedges:
      (none)
  Member "dynamical isometry and a mean field theory of lstms and grus":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "provable benefit of orthogonal initialization in optimizing deep linear networks")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "character level language modeling with deeper self attention":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient content based sparse attention with routing transformers")
  Member "bridging nonlinearities and stochastic regularizers with gaussian error linear units":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "xgpt cross modal generative pre training for image captioning")
  Member "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "soft root sign activation function")

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)