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: "part of speech tagging for code switched data"
  Abstract: We address the problem of Part of Speech tagging (POS) in the context of linguistic code switching (CS). CS is the phenomenon where a speaker switches between two languages or variants of the same language within or across utterances, known as intra-sentential or inter-sentential CS, respectively. Processing CS data is especially challenging in intra-sentential data given state of the art monolingual NLP technology since such technology is geared toward the processing of one language at a time. In this paper we explore multiple strategies of applying state of the art POS taggers to CS data. We investigate the landscape in two CS language pairs, Spanish-English and Modern Standard Arabic-Arabic dialects. We compare the use of two POS taggers vs. a unified tagger trained on CS data. Our results show that applying a machine learning framework using two state of the art POS taggers achieves better performance compared to all other approaches that we investigate.

Connected through hyperedge HE-1 (induced by source paper "leveraging pretrained word embeddings for part of speech tagging of code switching data"):
  with members:
    - "named entity recognition on code switched data overview of the calcs 2018 shared task"
    - "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-2 (induced by source paper "named entity recognition on code switched data overview of the calcs 2018 shared task"):
  with members:
    - "modeling noisiness to recognize named entities using multitask neural networks on social media"
    - "a multi task approach for named entity recognition in social media data"
    - "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-3 (induced by source paper "a dataset for building code mixed goal oriented conversation systems"):
  with members:
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "a persona based neural conversation model"
    - "learning end to end goal oriented dialog"
    - "query reduction networks for question answering"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "a survey of available corpora for building data driven dialogue systems"

Connected through hyperedge HE-4 (induced by source paper "crowdsourcing universal part of speech tags for code switching"):
  with members:
    - "a universal part of speech tagset"

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)