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: "learning deep transformer models for machine translation"
  Abstract: Transformer is the state-of-the-art model in recent machine translation evaluations. Two strands of research are promising to improve models of this kind: the first uses wide networks (a.k.a. Transformer-Big) and has been the de facto standard for the development of the Transformer system, and the other uses deeper language representation but faces the difficulty arising from learning deep networks. Here, we continue the line of research on the latter. We claim that a truly deep Transformer model can surpass the Transformer-Big counterpart by 1) proper use of layer normalization and 2) a novel way of passing the combination of previous layers to the next. On WMT'16 English- German, NIST OpenMT'12 Chinese-English and larger WMT'18 Chinese-English tasks, our deep system (30/25-layer encoder) outperforms the shallow Transformer-Big/Base baseline (6-layer encoder) by 0.4-2.4 BLEU points. As another bonus, the deep model is 1.6X smaller in size and 3X faster in training than Transformer-Big.

Connected through hyperedge HE-1 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers"):
  with members:
    - "scaling laws for neural language models"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "checkmate breaking the memory wall with optimal tensor rematerialization"
    - "megatron lm training multi billion parameter language models using model parallelism"

Connected through hyperedge HE-2 (induced by source paper "transformers as soft reasoners over language"):
  with members:
    - "differentiable reasoning on large knowledge bases and natural language"
    - "nlprolog reasoning with weak unification for question answering in natural language"
    - "probing natural language inference models through semantic fragments"
    - "improving graph neural network representations of logical formulae with subgraph pooling"
    - "analysing mathematical reasoning abilities of neural models"
    - "establishing strong baselines for the new decade sequence tagging syntactic and semantic parsing with bert"
    - "quartz an open domain dataset of qualitative relationship questions"
    - "clutrr a diagnostic benchmark for inductive reasoning from text"

Connected through hyperedge HE-3 (induced by source paper "neural machine translation with joint representation"):
  with members:
    - "towards two dimensional sequence to sequence model in neural machine translation"
    - "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "understanding deep learning requires rethinking generalization"
    - "deep residual learning for image recognition"
    - "neural machine translation of rare words with subword units"
    - "grid long short term memory"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "reducing transformer depth on demand with structured dropout"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "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"
    - "very deep self attention networks for end to end speech recognition"
    - "pay less attention with lightweight and dynamic convolutions"
    - "pre trained language model representations for language generation"
    - "well read students learn better on the importance of pre training compact models"

Connected through hyperedge HE-5 (induced by source paper "multi graph transformer for free hand sketch recognition"):
  with members:
    - "semantically tied paired cycle consistency for zero shot sketch based image retrieval"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "livesketch query perturbations for guided sketch based visual search"
    - "transformer xl attentive language models beyond a fixed length context"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "bp transformer modelling long range context via binary partitioning"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-6 (induced by source paper "character based nmt with transformer"):
  with members:
    - "naver labs europe s systems for the wmt19 machine translation robustness task"
    - "improving robustness of machine translation with synthetic noise"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "revisiting character based neural machine translation with capacity and compression"
    - "learning to segment inputs for nmt favors character level processing"
    - "training deeper neural machine translation models with transparent attention"
    - "character level language modeling with deeper self attention"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-7 (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"
    - "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"
    - "xlnet generalized autoregressive pretraining for language understanding"

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)