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: "adapting word embeddings to new languages with morphological and phonological subword representations"
  Abstract: Much work in Natural Language Processing (NLP) has been for resource-rich languages, making generalization to new, less-resourced languages challenging. We present two approaches for improving generalization to low-resourced languages by adapting continuous word representations using linguistically motivated subword units: phonemes, morphemes and graphemes. Our method requires neither parallel corpora nor bilingual dictionaries and provides a significant gain in performance over previous methods relying on these resources. We demonstrate the effectiveness of our approaches on Named Entity Recognition for four languages, namely Uyghur, Turkish, Bengali and Hindi, of which Uyghur and Bengali are low resource languages, and also perform experiments on Machine Translation. Exploiting subwords with transfer learning gives us a boost of +15.2 NER F1 for Uyghur and +9.7 F1 for Bengali. We also show improvements in the monolingual setting where we achieve (avg.) +3 F1 and (avg.) +1.35 BLEU.

Connected through hyperedge HE-1 (induced by source paper "language independent tokenisation rivals language specific tokenisation for word similarity prediction"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "word similarity datasets for thai construction and evaluation"
    - "a systematic study of leveraging subword information for learning word representations"
    - "how multilingual is multilingual bert"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "generalizing word embeddings using bag of subwords"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "comparing cnn and lstm character level embeddings in bilstm crf models for chemical and disease named entity recognition"

Connected through hyperedge HE-2 (induced by source paper "on the importance of subword information for morphological tasks in truly low resource languages"):
  with members:
    - "morphological word embeddings"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "a systematic study of leveraging subword information for learning word representations"
    - "modeling language variation and universals a survey on typological linguistics for natural language processing"
    - "generalizing word embeddings using bag of subwords"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "subword regularization improving neural network translation models with multiple subword candidates"

Connected through hyperedge HE-3 (induced by source paper "a systematic study of leveraging subword information for learning word representations"):
  with members:
    - "morphological word embeddings"
    - "learning semantic representations for novel words leveraging both form and context"
    - "generalizing word embeddings using bag of subwords"
    - "deep contextualized word representations"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "from characters to words to in between do we capture morphology"
    - "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"
    - "attention is all you need"

Connected through hyperedge HE-4 (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"
    - "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"
    - "polyglot neural language models a case study in cross lingual phonetic representation learning"

Connected through hyperedge HE-5 (induced by source paper "entity projection via machine translation for cross lingual ner"):
  with members:
    - "pioner datasets and baselines for armenian named entity recognition"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"
    - "the iit bombay english hindi parallel corpus"
    - "word translation without parallel data"
    - "neural architectures for named entity recognition"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "polyglot ner massive multilingual named entity recognition"

Connected through hyperedge HE-6 (induced by source paper "massive vs curated word embeddings for low resourced languages the case of yoruba and twi"):
  with members:
    - "camembert a tasty french language model"
    - "cross lingual language model pretraining"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "deep contextualized word representations"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-7 (induced by source paper "character eyes seeing language through character level taggers"):
  with members:
    - "indicatements that character language models learn english morpho syntactic units and regularities"
    - "deep contextualized word representations"
    - "learning to generate reviews and discovering sentiment"
    - "from characters to words to in between do we capture morphology"
    - "mimicking word embeddings using subword rnns"
    - "enriching word vectors with subword information"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-8 (induced by source paper "a little annotation does a lot of good a study in bootstrapping low resource named entity recognizers"):
  with members:
    - "deep contextualized word representations"
    - "deep active learning for named entity recognition"
    - "model transfer for tagging low resource languages using a bilingual dictionary"
    - "word translation without parallel data"
    - "massively multilingual word embeddings"
    - "neural architectures for named entity recognition"
    - "natural language processing almost from scratch"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

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)