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: "a survey on recent advances in named entity recognition from deep learning models"
  Abstract: Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. NER systems have been studied and developed widely for decades, but accurate systems using deep neural networks (NN) have only been introduced in the last few years. We present a comprehensive survey of deep neural network architectures for NER, and contrast them with previous approaches to NER based on feature engineering and other supervised or semi-supervised learning algorithms. Our results highlight the improvements achieved by neural networks, and show how incorporating some of the lessons learned from past work on feature-based NER systems can yield further improvements.

Connected through hyperedge HE-1 (induced by source paper "span based joint entity and relation extraction with transformer pre training"):
  with members:
    - "spanbert improving pre training by representing and predicting spans"
    - "scibert a pretrained language model for scientific text"
    - "neural metric learning for fast end to end relation extraction"
    - "entity relation extraction as multi turn question answering"
    - "mass masked sequence to sequence pre training for language generation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "end to end neural relation extraction using deep biaffine attention"
    - "a general framework for information extraction using dynamic span graphs"

Connected through hyperedge HE-2 (induced by source paper "deep contextualized self training for low resource dependency parsing"):
  with members:
    - "viable dependency parsing as sequence labeling"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "no training required exploring random encoders for sentence classification"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "distant supervision from disparate sources for low resource part of speech tagging"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "portuguese named entity recognition using bert crf"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "albert a lite bert for self supervised learning of language representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "towards open domain named entity recognition via neural correction models"):
  with members:
    - "cloze driven pretraining of self attention networks"
    - "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"
    - "evaluating the utility of hand crafted features in sequence labelling"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-5 (induced by source paper "named entity recognition is there a glass ceiling"):
  with members:
    - "graph convolution for multimodal information extraction from visually rich documents"
    - "a survey on deep learning for named entity recognition"
    - "chargrid towards understanding 2d documents"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "a survey on open information extraction"
    - "snorkel rapid training data creation with weak supervision"

Connected through hyperedge HE-6 (induced by source paper "knowledge guided named entity recognition"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scibert a pretrained language model for scientific text"
    - "question answering is a format when is it useful"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "exploring ways to incorporate additional knowledge to improve natural language commonsense question answering"
    - "careful selection of knowledge to solve open book question answering"
    - "the natural language decathlon multitask learning as question answering"

Connected through hyperedge HE-7 (induced by source paper "context aware entity linking with attentive neural networks on wikidata knowledge graph"):
  with members:
    - "opentapioca lightweight entity linking for wikidata"
    - "end to end neural entity linking"
    - "deeptype multilingual entity linking by neural type system evolution"
    - "syntax aware language modeling with recurrent neural networks"
    - "attention is all you need"
    - "deep joint entity disambiguation with local neural attention"
    - "effective approaches to attention based neural machine translation"
    - "tagme on the fly annotation of short text fragments by wikipedia entities"

Connected through hyperedge HE-8 (induced by source paper "sept improving scientific named entity recognition with span representation"):
  with members:
    - "a general framework for information extraction using dynamic span graphs"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end neural coreference resolution"
    - "end to end sequence labeling via bi directional lstm cnns crf"

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)