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 "widening the representation bottleneck in neural machine translation with lexical shortcuts"):
  Abstract: The transformer is a state-of-the-art neural translation model that uses attention to iteratively refine lexical representations with information drawn from the surrounding context. Lexical features are fed into the first layer and propagated through a deep network of hidden layers. We argue that the need to represent and propagate lexical features in each layer limits the model's capacity for learning and representing other information relevant to the task. To alleviate this bottleneck, we introduce gated shortcut connections between the embedding layer and each subsequent layer within the encoder and decoder. This enables the model to access relevant lexical content dynamically, without expending limited resources on storing it within intermediate states. We show that the proposed modification yields consistent improvements over a baseline transformer on standard WMT translation tasks in 5 translation directions (0.9 BLEU on average) and reduces the amount of lexical information passed along the hidden layers. We furthermore evaluate different ways to integrate lexical connections into the transformer architecture and present ablation experiments exploring the effect of proposed shortcuts on model behavior.

Includes members:
  Member "universal transformers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "exploiting deep representations for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gret global representation enhanced transformer")
  Member "tensor2tensor for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stress test evaluation of transformer based models in natural language understanding tasks")
  Member "why self attention a targeted evaluation of neural machine translation architectures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta embeddings based on self attention")
  Member "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "linguistic knowledge and transferability of contextual representations")
  Member "a call for clarity in reporting bleu scores":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multilingual denoising pre training for neural machine translation")
  Member "training deeper neural machine translation models with transparent attention":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "marathi to english neural machine translation with near perfect corpus and transformers")
  Member "multi representation ensembles and delayed sgd updates improve syntax based nmt":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimizing deep learning inference on embedded systems through adaptive model selection")

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)