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 "from english to code switching transfer learning with strong morphological clues"):
  Abstract: Code-switching is still an understudied phenomenon in natural language processing mainly because of two related challenges: it lacks annotated data, and it combines a vast diversity of low-resource languages. Despite the language diversity, many code-switching scenarios occur in language pairs, and English is often a common factor among them. In the first part of this paper, we use transfer learning from English to English-paired code-switched languages for the language identification (LID) task by applying two simple yet effective techniques: 1) a hierarchical attention mechanism that enhances morphological clues from character n-grams, and 2) a secondary loss that forces the model to learn n-gram representations that are particular to the languages involved. We use the bottom layers of the ELMo architecture to learn these morphological clues by essentially recognizing what is and what is not English. Our approach outperforms the previous state of the art on Nepali-English, Spanish-English, and Hindi-English datasets. In the second part of the paper, we use our best LID models for the tasks of Spanish-English named entity recognition and Hindi-English part-of-speech tagging by replacing their inference layers and retraining them. We show that our retrained models are capable of using the code-switching information on both tasks to outperform models that do not have such knowledge.

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 "subword level language identification for intra word code switching":
    also connected through other hyperedges:
      (none)
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "convolutional sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "enriching word vectors with subword information":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "neural machine translation by jointly learning to align and translate":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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)