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: "weighted transformer network for machine translation"
  Abstract: State-of-the-art results on neural machine translation often use attentional sequence-to-sequence models with some form of convolution or recursion. Vaswani et. al. (2017) propose a new architecture that avoids recurrence and convolution completely. Instead, it uses only self-attention and feed-forward layers. While the proposed architecture achieves state-of-the-art results on several machine translation tasks, it requires a large number of parameters and training iterations to converge. We propose Weighted Transformer, a Transformer with modified attention layers, that not only outperforms the baseline network in BLEU score but also converges 15-40% faster. Specifically, we replace the multi-head attention by multiple self-attention branches that the model learns to combine during the training process. Our model improves the state-of-the-art performance by 0.5 BLEU points on the WMT 2014 English-to-German translation task and by 0.4 on the English-to-French translation task.

Connected through hyperedge HE-1 (induced by source paper "meta embeddings based on self attention"):
  with members:
    - "time aware large kernel convolutions"
    - "pay less attention with lightweight and dynamic convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "improving neural language modeling via adversarial training"
    - "generating long sequences with sparse transformers"
    - "adaptive attention span in transformers"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "self attention with relative position representations"

Connected through hyperedge HE-2 (induced by source paper "time aware large kernel convolutions"):
  with members:
    - "accelerating large kernel convolution using summed area tables"
    - "pay less attention with lightweight and dynamic convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "massively multilingual neural machine translation"
    - "fast parametric learning with activation memorization"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "scaling neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "residual attention net for superior cross domain time sequence modeling"):
  with members:
    - "deep neural network ensembles for time series classification"
    - "insights into lstm fully convolutional networks for time series classification"
    - "multivariate lstm fcns for time series classification"
    - "ts chief a scalable and accurate forest algorithm for time series classification"
    - "star transformer"
    - "universal transformers"
    - "linked dynamic graph cnn learning on point cloud via linking hierarchical features"
    - "financial series prediction using attention lstm"

Connected through hyperedge HE-4 (induced by source paper "fairseq a fast extensible toolkit for sequence modeling"):
  with members:
    - "strategies for structuring story generation"
    - "mixture models for diverse machine translation tricks of the trade"
    - "fast parametric learning with activation memorization"
    - "a stable and effective learning strategy for trainable greedy decoding"
    - "self attention with relative position representations"
    - "bottom up abstractive summarization"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "scaling neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "fully quantized transformer for improved translation"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "scaling neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "groupreduce block wise low rank approximation for neural language model shrinking"
    - "block sparse recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "recurrent graph syntax encoder for neural machine translation"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "syntax enhanced neural machine translation with syntax aware word representations"
    - "semantic neural machine translation using amr"
    - "tied multitask learning for neural speech translation"
    - "self attention with relative position representations"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "graph to sequence learning using gated graph neural networks"
    - "n ary relation extraction using graph state lstm"

Connected through hyperedge HE-7 (induced by source paper "understanding and improving transformer from a multi particle dynamic system point of view"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fixup initialization residual learning without normalization"
    - "universal transformers"
    - "self attention with relative position representations"
    - "dynamically unfolding recurrent restorer a moving endpoint control method for image restoration"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "scaling neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "the evolved transformer"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "regularized evolution for image classifier architecture search"
    - "scaling neural machine translation"
    - "deep contextualized word representations"
    - "snas stochastic neural architecture search"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a call for clarity in reporting bleu scores"

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.AI(Artificial Intelligence)