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 multi task approach for named entity recognition in social media data"
  Abstract: Named Entity Recognition for social media data is challenging because of its inherent noisiness. In addition to improper grammatical structures, it contains spelling inconsistencies and numerous informal abbreviations. We propose a novel multi-task approach by employing a more general secondary task of Named Entity (NE) segmentation together with the primary task of fine-grained NE categorization. The multi-task neural network architecture learns higher order feature representations from word and character sequences along with basic Part-of-Speech tags and gazetteer information. This neural network acts as a feature extractor to feed a Conditional Random Fields classifier. We were able to obtain the first position in the 3rd Workshop on Noisy User-generated Text (WNUT-2017) with a 41.86% entity F1-score and a 40.24% surface F1-score.

Connected through hyperedge HE-1 (induced by source paper "why attention analyze bilstm deficiency and its remedies in the case of ner"):
  with members:
    - "discontinuous constituency parsing with a stack free transition system and a dynamic oracle"
    - "modeling noisiness to recognize named entities using multitask neural networks on social media"
    - "robust lexical features for improved neural network named entity recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "fast and accurate entity recognition with iterated dilated convolutions"

Connected through hyperedge HE-2 (induced by source paper "pretrained language model transfer on neural named entity recognition in indonesian conversational texts"):
  with members:
    - "on the strength of character language models for multilingual named entity recognition"
    - "deep contextualized word representations"
    - "deep rnns encode soft hierarchical syntax"
    - "colorless green recurrent networks dream hierarchically"
    - "universal language model fine tuning for text classification"
    - "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks"
    - "semi supervised multitask learning for sequence labeling"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"

Connected through hyperedge HE-3 (induced by source paper "aiding intra text representations with visual context for multimodal named entity recognition"):
  with members:
    - "revisiting cross modal retrieval"
    - "learning word vectors for 157 languages"
    - "multimodal named entity recognition for short social media posts"
    - "efficient large scale multi modal classification"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "dual attention networks for multimodal reasoning and matching"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-4 (induced by source paper "a survey of code switched speech and language processing"):
  with members:
    - "named entity recognition on code switched data overview of the calcs 2018 shared task"
    - "building a unified code switching asr system for south african languages"
    - "hindi english code switching speech corpus"
    - "acoustic and textual data augmentation for improved asr of code switching speech"
    - "a dataset for building code mixed goal oriented conversation systems"
    - "study of semi supervised approaches to improving english mandarin code switching speech recognition"
    - "joining hands exploiting monolingual treebanks for parsing of code mixing data"
    - "crowdsourcing universal part of speech tags for code switching"

Connected through hyperedge HE-5 (induced by source paper "named entity recognition on code switched data overview of the calcs 2018 shared task"):
  with members:
    - "part of speech tagging for code switched data"
    - "modeling noisiness to recognize named entities using multitask neural networks on social media"
    - "empower sequence labeling with task aware neural language model"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "enriching word vectors with subword information"
    - "neural architectures for named entity recognition"
    - "analysis of named entity recognition and linking for tweets"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

Connected through hyperedge HE-6 (induced by source paper "linguistically informed relation extraction and neural architectures for nested named entity recognition in bionlp ost 2019"):
  with members:
    - "neural relation extraction within and across sentence boundaries"
    - "lisa explaining recurrent neural network judgments via layer wise semantic accumulation and example to pattern transformation"
    - "joint bootstrapping machines for high confidence relation extraction"
    - "cross sentence n ary relation extraction with graph lstms"
    - "semi supervised multitask learning for sequence labeling"
    - "enriching word vectors with subword information"
    - "neural architectures for named entity recognition"
    - "classifying relations by ranking with convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "content based features to rank influential hidden services of the tor darknet"):
  with members:
    - "rosf leveraging information retrieval and supervised learning for recommending code snippets"
    - "the language of legal and illegal activity on the darknet"
    - "starspace embed all the things"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "empirical evaluation of rectified activations in convolutional network"
    - "content and popularity analysis of tor hidden services"
    - "a model of internet topology using k shell decomposition"

Connected through hyperedge HE-8 (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"

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)