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 "massively parallel cross lingual learning in low resource target language translation"):
  Abstract: We work on translation from rich-resource languages to low-resource languages. The main challenges we identify are the lack of low-resource language data, effective methods for cross-lingual transfer, and the variable-binding problem that is common in neural systems. We build a translation system that addresses these challenges using eight European language families as our test ground. Firstly, we add source and target family labels and study intra- family and inter-family influences for effective cross-lingual transfer. We achieve improvement of +8.4 BLEU score compared to single-family multi-source multi- target NMT baseline. We find that training two neighboring families closest to the low-resource language is often enough. Secondly, we construct an ablation study and find that reasonably good results can be achieved even with considerably less target data. Thirdly, we address the variable-binding problem by building an order-preserving named entity translation model. We obtain 60.6% accuracy in qualitative evaluation where our translations are akin to human translations in a preliminary study.

Includes members:
  Member "embedding learning through multilingual concept induction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond")
  Member "what level of quality can neural machine translation attain on literary text":
    also connected through other hyperedges:
      (none)
  Member "opennmt open source toolkit for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "prophetnet predicting future n gram for sequence to sequence pre training")
  Member "attention strategies for multi source sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "vatex a large scale high quality multilingual dataset for video and language research")
  Member "improving lexical choice in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "normalization of input output shared embeddings in text generation models")
  Member "deep learning scaling is predictable empirically":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fixmatch simplifying semi supervised learning with consistency and confidence")
  Member "toward multilingual neural machine translation with universal encoder and decoder":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "polyglot neural language models a case study in cross lingual phonetic representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using interlinear glosses as pivot in low resource multilingual machine translation")

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)