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 "on the effect of word order on cross lingual sentiment analysis"):
  Abstract: Current state-of-the-art models for sentiment analysis make use of word order either explicitly by pre-training on a language modeling objective or implicitly by using recurrent neural networks (RNNs) or convolutional networks (CNNs). This is a problem for cross-lingual models that use bilingual embeddings as features, as the difference in word order between source and target languages is not resolved. In this work, we explore reordering as a pre-processing step for sentence-level cross-lingual sentiment classification with two language combinations (English-Spanish, English-Catalan). We find that while reordering helps both models, CNNS are more sensitive to local reorderings, while global reordering benefits RNNs.

Includes members:
  Member "unsupervised statistical machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "do all roads lead to rome understanding the role of initialization in iterative back translation")
  Member "bilingual sentiment embeddings joint projection of sentiment across languages":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "irony detection in a multilingual context")
  Member "multibooked a corpus of basque and catalan hotel reviews annotated for aspect level sentiment classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "embedding projection for targeted cross lingual sentiment model comparisons and a real world study")
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings":
    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 "universal language model fine tuning for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "description based text classification with reinforcement learning")
  Member "phrase based neural unsupervised machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multilingual denoising pre training for neural machine translation")
  Member "unsupervised machine translation using monolingual corpora only":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised question decomposition for question answering")

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)