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: "model transfer for tagging low resource languages using a bilingual dictionary"
  Abstract: Cross-lingual model transfer is a compelling and popular method for predicting annotations in a low-resource language, whereby parallel corpora provide a bridge to a high-resource language and its associated annotated corpora. However, parallel data is not readily available for many languages, limiting the applicability of these approaches. We address these drawbacks in our framework which takes advantage of cross-lingual word embeddings trained solely on a high coverage bilingual dictionary. We propose a novel neural network model for joint training from both sources of data based on cross-lingual word embeddings, and show substantial empirical improvements over baseline techniques. We also propose several active learning heuristics, which result in improvements over competitive benchmark methods.

Connected through hyperedge HE-1 (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-2 (induced by source paper "unsupervised domain adaptation of contextualized embeddings for sequence labeling"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "learning and evaluating general linguistic intelligence"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "using similarity measures to select pretraining data for ner"
    - "unsupervised data augmentation for consistency training"
    - "evaluating historical text normalization systems how well do they generalize"

Connected through hyperedge HE-3 (induced by source paper "low resource sequence labeling via unsupervised multilingual contextualized representations"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural cross lingual named entity recognition with minimal resources"
    - "demystifying neural style transfer"
    - "attention is all you need"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"

Connected through hyperedge HE-4 (induced by source paper "massively multilingual transfer for ner"):
  with members:
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "distant supervision from disparate sources for low resource part of speech tagging"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "learning how to active learn a deep reinforcement learning approach"
    - "snorkel rapid training data creation with weak supervision"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"
    - "enriching word vectors with subword information"
    - "learning crosslingual word embeddings without bilingual corpora"

Connected through hyperedge HE-5 (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"
    - "adapting word embeddings to new languages with morphological and phonological subword representations"
    - "deep active learning for named entity recognition"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "a grounded unsupervised universal part of speech tagger for low resource languages"):
  with members:
    - "unsupervised neural machine translation"
    - "deep biaffine attention for neural dependency parsing"
    - "enriching word vectors with subword information"
    - "many languages one parser"
    - "transition based dependency parsing with stack long short term memory"
    - "named entity recognition with bidirectional lstm cnns"

Connected through hyperedge HE-7 (induced by source paper "modeling language variation and universals a survey on typological linguistics for natural language processing"):
  with members:
    - "morphological word embeddings"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "xnli evaluating cross lingual sentence representations"
    - "a deep generative model of vowel formant typology"
    - "deep contextualized word representations"
    - "deep probabilistic logic a unifying framework for indirect supervision"
    - "adversarial propagation and zero shot cross lingual transfer of word vector specialization"
    - "past present future a computational investigation of the typology of tense in 1000 languages"

Connected through hyperedge HE-8 (induced by source paper "low resource text classification using domain adversarial learning"):
  with members:
    - "lessons from the bible on modern topics low resource multilingual topic model evaluation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "adversarially regularized autoencoders"
    - "adversarial generation of natural language"
    - "semi supervised sequence tagging with bidirectional language models"
    - "adversarial learning for neural dialogue generation"
    - "adversarially regularized autoencoders for generating discrete structures"

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)