human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "hierarchical meta embeddings for code switching named entity recognition"):
  Abstract: In countries that speak multiple main languages, mixing up different languages within a conversation is commonly called code-switching. Previous works addressing this challenge mainly focused on word-level aspects such as word embeddings. However, in many cases, languages share common subwords, especially for closely related languages, but also for languages that are seemingly irrelevant. Therefore, we propose Hierarchical Meta-Embeddings (HME) that learn to combine multiple monolingual word-level and subword-level embeddings to create language-agnostic lexical representations. On the task of Named Entity Recognition for English-Spanish code-switching data, our model achieves the state-of-the-art performance in the multilingual settings. We also show that, in cross-lingual settings, our model not only leverages closely related languages, but also learns from languages with different roots. Finally, we show that combining different subunits are crucial for capturing code-switching entities.

Includes members:
  Member "named entity recognition on code switched data overview of the calcs 2018 shared task":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "xpersona evaluating multilingual personalized chatbot")
  Member "incorporating word and subword units in unsupervised machine translation using language model rescoring":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "zero resource cross domain named entity recognition")
  Member "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "meta embeddings based on self attention")
  Member "code switching language modeling using syntax aware multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a deep generative model for code switched text")
  Member "dynamic meta embeddings for improved sentence representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta embeddings based on self attention")
  Member "emo2vec learning generalized emotion representation by multi task training":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to memorize in neural task oriented dialogue systems")
  Member "learning word vectors for 157 languages":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "think globally embed locally locally linear meta embedding of words":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "meta embeddings based on self attention")

Question: Please tell me which class the source paper 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)