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 transfer languages for cross lingual learning"):
  Abstract: Cross-lingual transfer, where a high-resource transfer language is used to improve the accuracy of a low-resource task language, is now an invaluable tool for improving performance of natural language processing (NLP) on low-resource languages. However, given a particular task language, it is not clear which language to transfer from, and the standard strategy is to select languages based on ad hoc criteria, usually the intuition of the experimenter. Since a large number of features contribute to the success of cross-lingual transfer (including phylogenetic similarity, typological properties, lexical overlap, or size of available data), even the most enlightened experimenter rarely considers all these factors for the particular task at hand. In this paper, we consider this task of automatically selecting optimal transfer languages as a ranking problem, and build models that consider the aforementioned features to perform this prediction. In experiments on representative NLP tasks, we demonstrate that our model predicts good transfer languages much better than ad hoc baselines considering single features in isolation, and glean insights on what features are most informative for each different NLP tasks, which may inform future ad hoc selection even without use of our method. Code, data, and pre-trained models are available at this https URL

Includes members:
  Member "xnmt the extensible neural machine translation toolkit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the weaknesses of reinforcement learning for neural machine translation")
  Member "distant supervision from disparate sources for low resource part of speech tagging":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep contextualized self training for low resource dependency parsing")
  Member "neural factor graph models for cross lingual morphological tagging":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cmu 01 at the sigmorphon 2019 shared task on crosslinguality and context in morphology")
  Member "near or far wide range zero shot cross lingual dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "do we need word order information for cross lingual sequence labeling")
  Member "zero shot neural transfer for cross lingual entity linking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "parameter space factorization for zero shot learning across tasks and languages")
  Member "on difficulties of cross lingual transfer with order differences a case study on dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "xpersona evaluating multilingual personalized chatbot")
  Member "subword regularization improving neural network translation models with multiple subword candidates":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fastai a layered api for deep learning")
  Member "when and why are pre trained word embeddings useful for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distill adapt distill training small in domain models for neural 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)