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: "neural cross lingual named entity recognition with minimal resources"
  Abstract: For languages with no annotated resources, unsupervised transfer of natural language processing models such as named-entity recognition (NER) from resource-rich languages would be an appealing capability. However, differences in words and word order across languages make it a challenging problem. To improve mapping of lexical items across languages, we propose a method that finds translations based on bilingual word embeddings. To improve robustness to word order differences, we propose to use self-attention, which allows for a degree of flexibility with respect to word order. We demonstrate that these methods achieve state-of-the-art or competitive NER performance on commonly tested languages under a cross-lingual setting, with much lower resource requirements than past approaches. We also evaluate the challenges of applying these methods to Uyghur, a low-resource language.

Connected through hyperedge HE-1 (induced by source paper "a comparison of architectures and pretraining methods for contextualized multilingual word embeddings"):
  with members:
    - "cross lingual language model pretraining"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "polyglot semantic role labeling"
    - "xnli evaluating cross lingual sentence representations"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "deep contextualized word representations"
    - "multi source cross lingual model transfer learning what to share"

Connected through hyperedge HE-2 (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"
    - "demystifying neural style transfer"
    - "attention is all you need"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "model transfer for tagging low resource languages using a bilingual dictionary"
    - "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-3 (induced by source paper "cross lingual transfer for distantly supervised and low resources indonesian ner"):
  with members:
    - "empirical evaluation of character based model on neural named entity recognition in indonesian conversational texts"
    - "toward a standardized and more accurate indonesian part of speech tagging"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "deep rnns encode soft hierarchical syntax"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised multitask learning for sequence labeling"

Connected through hyperedge HE-4 (induced by source paper "few shot classification in named entity recognition task"):
  with members:
    - "sequence labeling a practical approach"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "label aware double transfer learning for cross specialty medical named entity recognition"
    - "learning how to self learn enhancing self training using neural reinforcement learning"
    - "deep active learning for named entity recognition"
    - "prototypical networks for few shot learning"
    - "improving one shot learning through fusing side information"

Connected through hyperedge HE-5 (induced by source paper "collecting indicators of compromise from unstructured text of cybersecurity articles using neural based sequence labelling"):
  with members:
    - "automatic identification of indicators of compromise using neural based sequence labelling"
    - "deep contextualized word representations"
    - "attention is all you need"
    - "de identification of medical records using conditional random fields and long short term memory networks"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "transfer learning for named entity recognition with neural networks"
    - "empower sequence labeling with task aware neural language model"
    - "de identification of patient notes with recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "towards lingua franca named entity recognition with bert"):
  with members:
    - "cloze driven pretraining of self attention networks"
    - "how multilingual is multilingual bert"
    - "robust lexical features for improved neural network named entity recognition"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-7 (induced by source paper "a survey on deep learning for named entity recognition"):
  with members:
    - "learning a unified named entity tagger from multiple partially annotated corpora for efficient adaptation"
    - "a unified mrc framework for named entity recognition"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "a little annotation does a lot of good a study in bootstrapping low resource named entity recognizers"
    - "dependency guided lstm crf for named entity recognition"
    - "hierarchical contextualized representation for named entity recognition"
    - "dice loss for data imbalanced nlp tasks"
    - "attending to entities for better text understanding"

Connected through hyperedge HE-8 (induced by source paper "cross lingual transfer learning for multilingual task oriented dialog"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "polyglot contextual representations improve crosslingual transfer"
    - "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"
    - "pytext a seamless path from nlp research to production"
    - "on the strength of character language models for multilingual named entity recognition"
    - "parameter sharing between dependency parsers for related languages"
    - "zero shot cross lingual classification using multilingual neural machine translation"

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)