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: "training tips for the transformer model"
  Abstract: This article describes our experiments in neural machine translation using the recent Tensor2Tensor framework and the Transformer sequence-to-sequence model (Vaswani et al., 2017). We examine some of the critical parameters that affect the final translation quality, memory usage, training stability and training time, concluding each experiment with a set of recommendations for fellow researchers. In addition to confirming the general mantra "more data and larger models", we address scaling to multiple GPUs and provide practical tips for improved training regarding batch size, learning rate, warmup steps, maximum sentence length and checkpoint averaging. We hope that our observations will allow others to get better results given their particular hardware and data constraints.

Connected through hyperedge HE-1 (induced by source paper "disentangling adaptive gradient methods from learning rates"):
  with members:
    - "the step decay schedule a near optimal geometrically decaying learning rate procedure for least squares"
    - "extreme tensoring for low memory preconditioning"
    - "on empirical comparisons of optimizers for deep learning"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "escaping saddle points with adaptive gradient methods"
    - "an exponential learning rate schedule for deep learning"
    - "deepobs a deep learning optimizer benchmark suite"

Connected through hyperedge HE-2 (induced by source paper "bert of theseus compressing bert by progressive module replacing"):
  with members:
    - "are sixteen heads really better than one"
    - "reducing transformer depth on demand with structured dropout"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "q bert hessian based ultra low precision quantization of bert"
    - "well read students learn better on the importance of pre training compact models"
    - "deep double descent where bigger models and more data hurt"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"

Connected through hyperedge HE-3 (induced by source paper "learning accurate integer transformer machine translation models"):
  with members:
    - "asymmetric valleys beyond sharp and flat local minima"
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "marian cost effective high quality neural machine translation in c"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "attention is all you need"
    - "deep residual learning for image recognition"
    - "neural machine translation by jointly learning to align and translate"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

Connected through hyperedge HE-4 (induced by source paper "exploring benefits of transfer learning in neural machine translation"):
  with members:
    - "widening the representation bottleneck in neural machine translation with lexical shortcuts"
    - "revisiting low resource neural machine translation a case study"
    - "tackling climate change with machine learning"
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "investigating backtranslation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "a bilingual generative transformer for semantic sentence embedding"):
  with members:
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "simple and effective paraphrastic similarity from parallel translations"
    - "beyond bleu training neural machine translation with semantic similarity"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "filtering and mining parallel data in a joint multilingual space"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"

Connected through hyperedge HE-6 (induced by source paper "on layer normalization in the transformer architecture"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "learning deep transformer models for machine translation"
    - "a mean field theory of batch normalization"
    - "on the variance of the adaptive learning rate and beyond"
    - "understanding and improving transformer from a multi particle dynamic system point of view"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fixup initialization residual learning without normalization"
    - "generating long sequences with sparse transformers"

Connected through hyperedge HE-7 (induced by source paper "data diversification an elegant strategy for neural machine translation"):
  with members:
    - "incorporating bert into neural machine translation"
    - "improving neural machine translation robustness via data augmentation beyond back translation"
    - "soft contextual data augmentation for neural machine translation"
    - "pay less attention with lightweight and dynamic convolutions"
    - "the evolved transformer"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "weighted transformer network for machine translation"
    - "self attention with relative position representations"

Connected through hyperedge HE-8 (induced by source paper "on the variance of the adaptive learning rate and beyond"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "fixup initialization residual learning without normalization"
    - "on the convergence of adam and beyond"
    - "roberta a robustly optimized bert pretraining approach"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "closing the generalization gap of adaptive gradient methods in training deep neural 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)