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 adaptation layers for cross domain named entity recognition"
  Abstract: Recent research efforts have shown that neural architectures can be effective in conventional information extraction tasks such as named entity recognition, yielding state-of-the-art results on standard newswire datasets. However, despite significant resources required for training such models, the performance of a model trained on one domain typically degrades dramatically when applied to a different domain, yet extracting entities from new emerging domains such as social media can be of significant interest. In this paper, we empirically investigate effective methods for conveniently adapting an existing, well-trained neural NER model for a new domain. Unlike existing approaches, we propose lightweight yet effective methods for performing domain adaptation for neural models. Specifically, we introduce adaptation layers on top of existing neural architectures, where no re-training using the source domain data is required. We conduct extensive empirical studies and show that our approach significantly outperforms state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "zero resource cross domain named entity recognition"):
  with members:
    - "do we need word order information for cross lingual sequence labeling"
    - "zero shot cross lingual dialogue systems with transferable latent variables"
    - "incorporating word and subword units in unsupervised machine translation using language model rescoring"
    - "attention informed mixed language training for zero shot cross lingual task oriented dialogue systems"
    - "robust zero shot cross domain slot filling with example values"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "label aware double transfer learning for cross specialty medical named entity recognition"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-2 (induced by source paper "rethinking generalization of neural models a named entity recognition case study"):
  with members:
    - "a closer look at data bias in neural extractive summarization models"
    - "transfer learning for sequence labeling using source model and target data"
    - "stiffness a new perspective on generalization in neural networks"
    - "design challenges and misconceptions in neural sequence labeling"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "state of the art chinese word segmentation with bi lstms"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (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-4 (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"
    - "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"
    - "an overview of multi task learning in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "learning to contextually aggregate multi source supervision for sequence labeling"):
  with members:
    - "crossweigh training named entity tagger from imperfect annotations"
    - "massively multilingual transfer for ner"
    - "multinomial adversarial networks for multi domain text classification"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "cross type biomedical named entity recognition with deep multi task learning"
    - "semi supervised sequence modeling with cross view training"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "who said what modeling individual labelers improves classification"

Connected through hyperedge HE-6 (induced by source paper "fine grained knowledge fusion for sequence labeling domain adaptation"):
  with members:
    - "chinese ner using lattice lstm"
    - "multi source domain adaptation with mixture of experts"
    - "information aggregation via dynamic routing for sequence encoding"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "learning to select data for transfer learning with bayesian optimization"
    - "bidirectional lstm crf models for sequence tagging"
    - "frustratingly easy domain adaptation"

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 "deep transfer reinforcement learning for text summarization"):
  with members:
    - "a practitioners guide to transfer learning for text classification using convolutional neural networks"
    - "deep reinforcement learning for sequence to sequence models"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "tensor2tensor for neural machine translation"
    - "the natural language decathlon multitask learning as question answering"
    - "neural abstractive text summarization with sequence to sequence models a survey"
    - "bottom up abstractive summarization"

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)