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: "code switching language modeling using syntax aware multi task learning"
  Abstract: Lack of text data has been the major issue on code-switching language modeling. In this paper, we introduce multi-task learning based language model which shares syntax representation of languages to leverage linguistic information and tackle the low resource data issue. Our model jointly learns both language modeling and Part-of-Speech tagging on code-switched utterances. In this way, the model is able to identify the location of code-switching points and improves the prediction of next word. Our approach outperforms standard LSTM based language model, with an improvement of 9.7% and 7.4% in perplexity on SEAME Phase I and Phase II dataset respectively.

Connected through hyperedge HE-1 (induced by source paper "hierarchical meta embeddings for code switching named entity recognition"):
  with members:
    - "named entity recognition on code switched data overview of the calcs 2018 shared task"
    - "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"
    - "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-2 (induced by source paper "a deep generative model for code switched text"):
  with members:
    - "improved sentiment detection via label transfer from monolingual to synthetic code switched text"
    - "code switched language models using dual rnns and same source pretraining"
    - "adversarial evaluation of dialogue models"
    - "generating sentences from a continuous space"
    - "character aware neural language models"
    - "a large annotated corpus for learning natural language inference"
    - "attention based models for speech recognition"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-3 (induced by source paper "code switched language models using neural based synthetic data from parallel sentences"):
  with members:
    - "code switched language models using dual rnns and same source pretraining"
    - "learning comment generation by leveraging user generated data"
    - "get to the point summarization with pointer generator networks"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "using the output embedding to improve language models"
    - "effective approaches to attention based neural machine translation"
    - "syntactic and semantic features for code switching factored language models"

Connected through hyperedge HE-4 (induced by source paper "learn to code switch data augmentation using copy mechanism on language modeling"):
  with members:
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "get to the point summarization with pointer generator networks"
    - "using the output embedding to improve language models"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "towards end to end automatic code switching speech recognition"):
  with members:
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "effective approaches to attention based neural machine translation"
    - "deep speech 2 end to end speech recognition in english and mandarin"

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)