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: "pay less attention with lightweight and dynamic convolutions"
  Abstract: Self-attention is a useful mechanism to build generative models for language and images. It determines the importance of context elements by comparing each element to the current time step. In this paper, we show that a very lightweight convolution can perform competitively to the best reported self-attention results. Next, we introduce dynamic convolutions which are simpler and more efficient than self-attention. We predict separate convolution kernels based solely on the current time-step in order to determine the importance of context elements. The number of operations required by this approach scales linearly in the input length, whereas self-attention is quadratic. Experiments on large-scale machine translation, language modeling and abstractive summarization show that dynamic convolutions improve over strong self-attention models. On the WMT'14 English-German test set dynamic convolutions achieve a new state of the art of 29.7 BLEU.

Connected through hyperedge HE-1 (induced by source paper "on feature normalization and data augmentation"):
  with members:
    - "convolutional networks with dense connectivity"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "implicit semantic data augmentation for deep networks"
    - "bertscore evaluating text generation with bert"
    - "ctrl a conditional transformer language model for controllable generation"
    - "natural adversarial examples"
    - "randaugment practical automated data augmentation with a reduced search space"

Connected through hyperedge HE-2 (induced by source paper "meta embeddings based on self attention"):
  with members:
    - "time aware large kernel 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"
    - "weighted transformer network for machine translation"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "self attention with relative position representations"

Connected through hyperedge HE-3 (induced by source paper "time aware large kernel convolutions"):
  with members:
    - "accelerating large kernel convolution using summed area tables"
    - "transformer xl attentive language models beyond a fixed length context"
    - "massively multilingual neural machine translation"
    - "fast parametric learning with activation memorization"
    - "weighted transformer network for machine translation"
    - "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-4 (induced by source paper "rethinking batch normalization in transformers"):
  with members:
    - "towards stabilizing batch statistics in backward propagation of batch normalization"
    - "improving deep transformer with depth scaled initialization and merged attention"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "a tensorized transformer for language modeling"
    - "reducing transformer depth on demand with structured dropout"
    - "root mean square layer normalization"
    - "understanding and improving layer normalization"
    - "transformer xl attentive language models beyond a fixed length context"

Connected through hyperedge HE-5 (induced by source paper "towards the systematic reporting of the energy and carbon footprints of machine learning"):
  with members:
    - "tackling climate change with machine learning"
    - "accelerating deep learning by focusing on the biggest losers"
    - "hardnet a low memory traffic network"
    - "energy and policy considerations for deep learning in nlp"
    - "quantifying the carbon emissions of machine learning"
    - "single headed attention rnn stop thinking with your head"
    - "green ai"
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"

Connected through hyperedge HE-6 (induced by source paper "low rank bottleneck in multi head attention models"):
  with members:
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "adaptively sparse transformers"
    - "are transformers universal approximators of sequence to sequence functions"
    - "token level ensemble distillation for grapheme to phoneme conversion"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "universal transformers"
    - "generating long sequences with sparse transformers"

Connected through hyperedge HE-7 (induced by source paper "incorporating bert into neural machine translation"):
  with members:
    - "towards making the most of bert in neural machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "the evolved transformer"
    - "distilling the knowledge of bert for text generation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "document level neural machine translation with hierarchical attention networks"
    - "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing"

Connected through hyperedge HE-8 (induced by source paper "normalization of input output shared embeddings in text generation models"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "levenshtein transformer"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "transformers without tears improving the normalization of self attention"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

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)