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 "mimicking word embeddings using subword rnns"):
  Abstract: Word embeddings improve generalization over lexical features by placing each word in a lower-dimensional space, using distributional information obtained from unlabeled data. However, the effectiveness of word embeddings for downstream NLP tasks is limited by out-of-vocabulary (OOV) words, for which embeddings do not exist. In this paper, we present MIMICK, an approach to generating OOV word embeddings compositionally, by learning a function from spellings to distributional embeddings. Unlike prior work, MIMICK does not require re-training on the original word embedding corpus; instead, learning is performed at the type level. Intrinsic and extrinsic evaluations demonstrate the power of this simple approach. On 23 languages, MIMICK improves performance over a word-based baseline for tagging part-of-speech and morphosyntactic attributes. It is competitive with (and complementary to) a supervised character-based model in low-resource settings.

Includes members:
  Member "morphological priors for probabilistic neural word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nytwit a dataset of novel words in the new york times")
  Member "enriching word vectors with subword information":
    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 "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distant supervision and noisy label learning for low resource named entity recognition a study on hausa and yor ub a")
  Member "charagram embedding words and sentences via character n grams":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving candidate generation for low resource cross lingual entity linking")
  Member "character aware neural language models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "trends and advancements in deep neural network communication")
  Member "morpho syntactic lexicon generation using graph based semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint semantic synthesis and morphological analysis of the derived word")
  Member "compositional morphology for word representations and language modelling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using interlinear glosses as pivot in low resource multilingual machine translation")
  Member "polyglot distributed word representations for multilingual nlp":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multilingual alignment of contextual word representations")

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)