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: "multi task domain adaptation for sequence tagging"
  Abstract: Many domain adaptation approaches rely on learning cross domain shared representations to transfer the knowledge learned in one domain to other domains. Traditional domain adaptation only considers adapting for one task. In this paper, we explore multi-task representation learning under the domain adaptation scenario. We propose a neural network framework that supports domain adaptation for multiple tasks simultaneously, and learns shared representations that better generalize for domain adaptation. We apply the proposed framework to domain adaptation for sequence tagging problems considering two tasks: Chinese word segmentation and named entity recognition. Experiments show that multi-task domain adaptation works better than disjoint domain adaptation for each task, and achieves the state-of-the-art results for both tasks in the social media domain.

Connected through hyperedge HE-1 (induced by source paper "improving pre trained multilingual models with vocabulary expansion"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "gromov wasserstein alignment of word embedding spaces"
    - "generating wikipedia by summarizing long sequences"
    - "bilingual expert can find translation errors"
    - "universal neural machine translation for extremely low resource languages"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "on the limitations of unsupervised bilingual dictionary induction"

Connected through hyperedge HE-2 (induced by source paper "gated task interaction framework for multi task sequence tagging"):
  with members:
    - "aspect based sentiment analysis with gated convolutional networks"
    - "linguistically informed self attention for semantic role labeling"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised multitask learning for sequence labeling"
    - "semi supervised sequence tagging with bidirectional language models"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"

Connected through hyperedge HE-3 (induced by source paper "improving sentiment analysis with multi task learning of negation"):
  with members:
    - "semeval 2013 task 2 sentiment analysis in twitter"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "a multi sentiment resource enhanced attention network for sentiment classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "transfer and multi task learning for noun noun compound interpretation"
    - "universal language model fine tuning for text classification"
    - "self attention a better building block for sentiment analysis neural network classifiers"

Connected through hyperedge HE-4 (induced by source paper "a joint named entity recognizer for heterogeneous tag setsusing a tag hierarchy"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "multi task learning of pairwise sequence classification tasks over disparate label spaces"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "transfer learning for named entity recognition with neural networks"
    - "an overview of multi task learning in deep neural networks"
    - "named entity recognition for novel types by transfer learning"
    - "de identification of patient notes with recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "multi task learning with contextualized word representations for extented named entity recognition"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "multi task learning for sequence tagging an empirical study"
    - "neural adaptation layers for cross domain named entity recognition"
    - "end to end recurrent neural network models for vietnamese named entity recognition word level vs character level"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised multitask learning for sequence labeling"

Connected through hyperedge HE-6 (induced by source paper "few shot and zero shot learning for historical text normalization"):
  with members:
    - "a large scale comparison of historical text normalization systems"
    - "tied multitask learning for neural speech translation"
    - "xnmt the extensible neural machine translation toolkit"
    - "an evaluation of neural machine translation models on historical spelling normalization"
    - "evaluating historical text normalization systems how well do they generalize"
    - "unimorph 2 0 universal morphology"
    - "scheduled multi task learning from syntax to translation"
    - "one shot neural cross lingual transfer for paradigm completion"

Connected through hyperedge HE-7 (induced by source paper "a self attentional neural architecture for code completion with multi task learning"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "code completion with neural attention and pointer networks"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "attention is all you need"
    - "an overview of multi task learning in deep neural networks"
    - "learning python code suggestion with a sparse pointer network"

Connected through hyperedge HE-8 (induced by source paper "joint entity extraction and assertion detection for clinical text"):
  with members:
    - "relation extraction using explicit context conditioning"
    - "comprehend medical a named entity recognition and relationship extraction web service"
    - "dynamic transfer learning for named entity recognition"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"
    - "negbio a high performance tool for negation and uncertainty detection in radiology reports"
    - "morphological priors for probabilistic neural word embeddings"
    - "enriching word vectors with subword information"
    - "multi task cross lingual sequence tagging from scratch"

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)