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: "named entity recognition on code switched data overview of the calcs 2018 shared task"
  Abstract: In the third shared task of the Computational Approaches to Linguistic Code-Switching (CALCS) workshop, we focus on Named Entity Recognition (NER) on code-switched social-media data. We divide the shared task into two competitions based on the English-Spanish (ENG-SPA) and Modern Standard Arabic-Egyptian (MSA-EGY) language pairs. We use Twitter data and 9 entity types to establish a new dataset for code-switched NER benchmarks. In addition to the CS phenomenon, the diversity of the entities and the social media challenges make the task considerably hard to process. As a result, the best scores of the competitions are 63.76% and 71.61% for ENG-SPA and MSA-EGY, respectively. We present the scores of 9 participants and discuss the most common challenges among submissions.

Connected through hyperedge HE-1 (induced by source paper "xpersona evaluating multilingual personalized chatbot"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "towards a human like open domain chatbot"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "code switched language models using neural based synthetic data from parallel sentences"
    - "unsupervised cross lingual representation learning at scale"
    - "camembert a tasty french language model"
    - "zero shot cross lingual dialogue systems with transferable latent variables"
    - "cross lingual ability of multilingual bert an empirical study"

Connected through hyperedge HE-2 (induced by source paper "leveraging pretrained word embeddings for part of speech tagging of code switching data"):
  with members:
    - "part of speech tagging for code switched data"
    - "creating a large multi layered representational repository of linguistic code switched arabic data"
    - "universal dependency parsing for hindi english code switching"
    - "joining hands exploiting monolingual treebanks for parsing of code mixing data"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "word translation without parallel data"
    - "enriching word vectors with subword information"
    - "stack propagation improved representation learning for syntax"

Connected through hyperedge HE-3 (induced by source paper "hierarchical meta embeddings for code switching named entity recognition"):
  with members:
    - "incorporating word and subword units in unsupervised machine translation using language model rescoring"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "code switching language modeling using syntax aware multi task learning"
    - "dynamic meta embeddings for improved sentence representations"
    - "emo2vec learning generalized emotion representation by multi task training"
    - "learning word vectors for 157 languages"
    - "think globally embed locally locally linear meta embedding of words"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "a survey of code switched speech and language processing"):
  with members:
    - "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"
    - "a multi task approach for named entity recognition in social media data"

Connected through hyperedge HE-5 (induced by source paper "from english to code switching transfer learning with strong morphological clues"):
  with members:
    - "subword level language identification for intra word code switching"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "convolutional sequence to sequence learning"
    - "enriching word vectors with subword information"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "codeswitch reddit exploration of written multilingual discourse in online discussion forums"):
  with members:
    - "building a unified code switching asr system for south african languages"
    - "multilingual constituency parsing with self attention and pre training"
    - "native language cognate effects on second language lexical choice"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "challenges of computational processing of code switching"

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)