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 "why deep transformers are difficult to converge from computation order to lipschitz restricted parameter initialization"):
  Abstract: The Transformer translation model employs residual connection and layer normalization to ease the optimization difficulties caused by its multi-layer encoder/decoder structure. While several previous works show that even with residual connection and layer normalization, deep Transformers still have difficulty in training, and particularly a Transformer model with more than 12 encoder/decoder layers fails to converge. In this paper, we first empirically demonstrate that a simple modification made in the official implementation which changes the computation order of residual connection and layer normalization can effectively ease the optimization of deep Transformers. In addition, we deeply compare the subtle difference in computation order, and propose a parameter initialization method which simply puts Lipschitz restriction on the initialization of Transformers but can effectively ensure their convergence. We empirically show that with proper parameter initialization, deep Transformers with the original computation order can converge, which is quite in contrast to all previous works, and obtain significant improvements with up to 24 layers. Our proposed approach additionally enables to benefit from deep decoders compared to previous works which focus on deep encoders.

Includes members:
  Member "improving deep transformer with depth scaled initialization and merged attention":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rethinking batch normalization in transformers")
  Member "neutron an implementation of the transformer translation model and its variants":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "uds submission for the wmt 19 automatic post editing task")
  Member "depth growing for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reducing transformer depth on demand with structured dropout")
  Member "training deeper neural machine translation models with transparent attention":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "marathi to english neural machine translation with near perfect corpus and transformers")
  Member "sockeye a toolkit for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "marathi to english neural machine translation with near perfect corpus and transformers")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "opennmt open source toolkit for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "prophetnet predicting future n gram for sequence to sequence pre training")
  Member "convolutional sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a brief survey of multilingual neural machine translation")

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.CL(Computation and Language)