human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "multi layer representation fusion for neural machine translation"
  Abstract: Neural machine translation systems require a number of stacked layers for deep models. But the prediction depends on the sentence representation of the top-most layer with no access to low-level representations. This makes it more difficult to train the model and poses a risk of information loss to prediction. In this paper, we propose a multi-layer representation fusion (MLRF) approach to fusing stacked layers. In particular, we design three fusion functions to learn a better representation from the stack. Experimental results show that our approach yields improvements of 0.92 and 0.56 BLEU points over the strong Transformer baseline on IWSLT German-English and NIST Chinese-English MT tasks respectively. The result is new state-of-the-art in German-English translation.

Connected through hyperedge HE-1 (induced by source paper "gret global representation enhanced transformer"):
  with members:
    - "context aware self attention networks"
    - "dynamic past and future for neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "deconvolution based global decoding for neural machine translation"
    - "exploiting deep representations for neural machine translation"
    - "global encoding for abstractive summarization"
    - "investigating capsule networks with dynamic routing for text classification"
    - "self attention with relative position representations"

Connected through hyperedge HE-2 (induced by source paper "dynamic layer aggregation for neural machine translation with routing by agreement"):
  with members:
    - "context aware self attention networks"
    - "neural machine translation with adequacy oriented learning"
    - "achieving human parity on automatic chinese to english news translation"
    - "exploiting deep representations for neural machine translation"
    - "tied multitask learning for neural speech translation"
    - "modeling localness for self attention networks"
    - "deep contextualized word representations"
    - "information aggregation via dynamic routing for sequence encoding"

Connected through hyperedge HE-3 (induced by source paper "acquiring knowledge from pre trained model to neural machine translation"):
  with members:
    - "learning representation mapping for relation detection in knowledge base question answering"
    - "mass masked sequence to sequence pre training for language generation"
    - "dynamic past and future for neural machine translation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "investigating backtranslation in neural machine translation"
    - "exploiting deep representations for neural machine translation"
    - "beyond error propagation in neural machine translation characteristics of language also matter"

Connected through hyperedge HE-4 (induced by source paper "learning deep transformer models for machine translation"):
  with members:
    - "exploiting deep representations for neural machine translation"
    - "self attention with relative position representations"
    - "scaling neural machine translation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "training deeper neural machine translation models with transparent attention"
    - "character level language modeling with deeper self attention"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "exploiting sentential context for neural machine translation"):
  with members:
    - "dynamic layer aggregation for neural machine translation with routing by agreement"
    - "information aggregation for multi head attention with routing by agreement"
    - "context aware self attention networks"
    - "universal transformers"
    - "modeling recurrence for transformer"
    - "deconvolution based global decoding for neural machine translation"
    - "exploiting deep representations for neural machine translation"
    - "modeling localness for self attention networks"

Connected through hyperedge HE-6 (induced by source paper "improving neural machine translation with pre trained representation"):
  with members:
    - "distilling task specific knowledge from bert into simple neural networks"
    - "investigating backtranslation in neural machine translation"
    - "exploiting deep representations for neural machine translation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "beyond error propagation in neural machine translation characteristics of language also matter"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "modeling recurrence for transformer"):
  with members:
    - "context aware self attention networks"
    - "universal transformers"
    - "achieving human parity on automatic chinese to english news translation"
    - "exploiting deep representations for neural machine translation"
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "context aware neural machine translation learns anaphora resolution"
    - "ordered neurons integrating tree structures into recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "self attention with structural position representations"):
  with members:
    - "exploiting sentential context for neural machine translation"
    - "towards better modeling hierarchical structure for self attention with ordered neurons"
    - "modeling recurrence for transformer"
    - "semantic neural machine translation using amr"
    - "adaptive attention span in transformers"
    - "exploiting deep representations for neural machine translation"
    - "self attention with relative position representations"
    - "modeling localness for self attention networks"

Question: Please tell me which class the center node 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)