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 "choosing the right word using bidirectional lstm tagger for writing support systems"):
  Abstract: Scientific writing is difficult. It is even harder for those for whom English is a second language (ESL learners). Scholars around the world spend a significant amount of time and resources proofreading their work before submitting it for review or publication. #R##N#In this paper we present a novel machine learning based application for proper word choice task. Proper word choice is a generalization the lexical substitution (LS) and grammatical error correction (GEC) tasks. We demonstrate and evaluate the usefulness of applying bidirectional Long Short Term Memory (LSTM) tagger, for this task. While state-of-the-art grammatical error correction uses error-specific classifiers and machine translation methods, we demonstrate an unsupervised method that is based solely on a high quality text corpus and does not require manually annotated data. We use a bidirectional Recurrent Neural Network (RNN) with LSTM for learning the proper word choice based on a word's sentential context. We demonstrate and evaluate our application on both a domain-specific (scientific), writing task and a general-purpose writing task. We show that our domain-specific and general-purpose models outperform state-of-the-art general context learning. As an additional contribution of this research, we also share our code, pre-trained models, and a new ESL learner test set with the research community.

Includes members:
  Member "outrageously large neural networks the sparsely gated mixture of experts layer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attention over parameters for dialogue systems")
  Member "lstm a search space odyssey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "fake news detection on social media a data mining perspective":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification")
  Member "w2vlda almost unsupervised system for aspect based sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "leveraging deep graph based text representation for sentiment polarity applications")
  Member "paraphrase generation with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "paraphrase generation with latent bag of words")
  Member "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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 "exploring the limits of language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")
  Member "pointer sentinel mixture models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the implicit and explicit regularization effects of dropout")

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)