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: "beam search strategies for neural machine translation"
  Abstract: The basic concept in Neural Machine Translation (NMT) is to train a large Neural Network that maximizes the translation performance on a given parallel corpus. NMT is then using a simple left-to-right beam-search decoder to generate new translations that approximately maximize the trained conditional probability. The current beam search strategy generates the target sentence word by word from left-to- right while keeping a fixed amount of active candidates at each time step. First, this simple search is less adaptive as it also expands candidates whose scores are much worse than the current best. Secondly, it does not expand hypotheses if they are not within the best scoring candidates, even if their scores are close to the best one. The latter one can be avoided by increasing the beam size until no performance improvement can be observed. While you can reach better performance, this has the draw- back of a slower decoding speed. In this paper, we concentrate on speeding up the decoder by applying a more flexible beam search strategy whose candidate size may vary at each time step depending on the candidate scores. We speed up the original decoder by up to 43% for the two language pairs German-English and Chinese-English without losing any translation quality.

Connected through hyperedge HE-1 (induced by source paper "urdu english machine transliteration using neural networks"):
  with members:
    - "how much does tokenization affect neural machine translation"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "attention is all you need"
    - "six challenges for neural machine translation"
    - "natural language processing state of the art current trends and challenges"
    - "neural machine transliteration preliminary results"
    - "transfer learning for low resource neural machine translation"
    - "polish to english statistical machine translation"

Connected through hyperedge HE-2 (induced by source paper "self attentional models application in task oriented dialogue generation systems"):
  with members:
    - "universal transformers"
    - "tensor2tensor for neural machine translation"
    - "generating wikipedia by summarizing long sequences"
    - "building a conversational agent overnight with dialogue self play"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "conversational ai the science behind the alexa prize"
    - "key value retrieval networks for task oriented dialogue"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "back attention knowledge transfer for low resource named entity recognition"):
  with members:
    - "linguistic knowledge and transferability of contextual representations"
    - "chinese ner using lattice lstm"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "convolutional sequence to sequence learning"
    - "learning word segmentation representations to improve named entity recognition for chinese social media"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-4 (induced by source paper "guiding theorem proving by recurrent neural networks"):
  with members:
    - "atpboost learning premise selection in binary setting with atp feedback"
    - "gamepad a learning environment for theorem proving"
    - "first experiments with neural translation of informal to formal mathematics"
    - "can neural networks understand logical entailment"
    - "reinforcement learning of theorem proving"
    - "enigma efficient learning based inference guiding machine"
    - "deep network guided proof search"
    - "holstep a machine learning dataset for higher order logic theorem proving"

Connected through hyperedge HE-5 (induced by source paper "superchat dialogue generation by transfer learning from vision to language using two dimensional word embedding"):
  with members:
    - "supertml two dimensional word embedding and transfer learning using imagenet pretrained cnn models for the classifications on tabular data"
    - "squared english word a method of generating glyph to use super characters for sentiment analysis"
    - "generating multiple diverse responses for short text conversation"
    - "ultra power efficient cnn domain specific accelerator with 9 3tops watt for mobile and embedded applications"
    - "super characters a conversion from sentiment classification to image classification"
    - "attention is all you need"
    - "squeeze and excitation networks"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-6 (induced by source paper "char2char generation with reranking for the e2e nlg challenge"):
  with members:
    - "neural amr sequence to sequence models for parsing and generation"
    - "referenceless quality estimation for natural language generation"
    - "learning to generate one sentence biographies from wikidata"
    - "massive exploration of neural machine translation architectures"
    - "the e2e dataset new challenges for end to end generation"
    - "natural language generation in dialogue using lexicalized and delexicalized data"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "a character level decoder without explicit segmentation for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "generating synthetic data for neural keyword to question models"):
  with members:
    - "attention based convolutional neural network for machine comprehension"
    - "learning classifiers from synthetic data using a multichannel autoencoder"
    - "scenenet understanding real world indoor scenes with synthetic data"
    - "adam a method for stochastic optimization"
    - "recurrent models of visual attention"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-8 (induced by source paper "beam search decoding using manner of articulation detection knowledge derived from connectionist temporal classification"):
  with members:
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "neural machine translation by jointly learning to align and translate"

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)