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: "polyglot neural language models a case study in cross lingual phonetic representation learning"
  Abstract: We introduce polyglot language models, recurrent neural network models trained to predict symbol sequences in many different languages using shared representations of symbols and conditioning on typological information about the language to be predicted. We apply these to the problem of modeling phone sequences---a domain in which universal symbol inventories and cross-linguistically shared feature representations are a natural fit. Intrinsic evaluation on held-out perplexity, qualitative analysis of the learned representations, and extrinsic evaluation in two downstream applications that make use of phonetic features show (i) that polyglot models better generalize to held-out data than comparable monolingual models and (ii) that polyglot phonetic feature representations are of higher quality than those learned monolingually.

Connected through hyperedge HE-1 (induced by source paper "using interlinear glosses as pivot in low resource multilingual machine translation"):
  with members:
    - "morphological word embeddings"
    - "character aware decoder for translation into morphologically rich languages"
    - "massively parallel cross lingual learning in low resource target language translation"
    - "improving character based decoding using target side morphological information for neural machine translation"
    - "opennmt open source toolkit for neural machine translation"
    - "six challenges for neural machine translation"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "multi way multilingual neural machine translation with a shared attention mechanism"

Connected through hyperedge HE-2 (induced by source paper "low resource parsing with crosslingual contextualized representations"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "polyglot contextual representations improve crosslingual transfer"
    - "linguistic knowledge and transferability of contextual representations"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "parameter sharing between dependency parsers for related languages"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "choosing transfer languages for cross lingual learning"):
  with members:
    - "xnmt the extensible neural machine translation toolkit"
    - "distant supervision from disparate sources for low resource part of speech tagging"
    - "neural factor graph models for cross lingual morphological tagging"
    - "near or far wide range zero shot cross lingual dependency parsing"
    - "zero shot neural transfer for cross lingual entity linking"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "when and why are pre trained word embeddings useful for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "polyglot contextual representations improve crosslingual transfer"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "universal dependency parsing from scratch"
    - "cross lingual language model pretraining"
    - "polyglot semantic role labeling"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "parameter sharing between dependency parsers for related languages"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "low resource machine translation using interlinear glosses"):
  with members:
    - "revisiting low resource neural machine translation a case study"
    - "character aware decoder for translation into morphologically rich languages"
    - "adapting word embeddings to new languages with morphological and phonological subword representations"
    - "improving character based decoding using target side morphological information for neural machine translation"
    - "opennmt open source toolkit for neural machine translation"
    - "six challenges for neural machine translation"
    - "ensembling factored neural machine translation models for automatic post editing and quality estimation"
    - "toward multilingual neural machine translation with universal encoder and decoder"

Connected through hyperedge HE-6 (induced by source paper "a systematic comparison of methods for low resource dependency parsing on genuinely low resource languages"):
  with members:
    - "data augmentation via dependency tree morphing for low resource languages"
    - "parameter sharing between dependency parsers for related languages"
    - "parser training with heterogeneous treebanks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "82 treebanks 34 models universal dependency parsing with multi treebank models"
    - "universal language model fine tuning for text classification"
    - "data augmentation for low resource neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "cmu 01 at the sigmorphon 2019 shared task on crosslinguality and context in morphology"):
  with members:
    - "a simple joint model for improved contextual neural lemmatization"
    - "75 languages 1 model parsing universal dependencies universally"
    - "modeling composite labels for neural morphological tagging"
    - "neural factor graph models for cross lingual morphological tagging"
    - "marrying universal dependencies and universal morphology"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improving named entity recognition by jointly learning to disambiguate morphological tags"
    - "contextual parameter generation for universal neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "acquisition of inflectional morphology in artificial neural networks with prior knowledge"):
  with members:
    - "probing what different nlp tasks teach machines about function word comprehension"
    - "neural transductive learning and beyond morphological generation in the minimal resource setting"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "recurrent neural networks in linguistic theory revisiting pinker and prince 1988 and the past tense debate"
    - "one shot neural cross lingual transfer for paradigm completion"
    - "get to the point summarization with pointer generator networks"
    - "towards linguistically generalizable nlp systems a workshop and shared task"
    - "incorporating copying mechanism in sequence to sequence learning"

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)