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 "the best of both worlds lexical resources to improve low resource part of speech tagging"):
  Abstract: In natural language processing, the deep learning revolution has shifted the focus from conventional hand-crafted symbolic representations to dense inputs, which are adequate representations learned automatically from corpora. However, particularly when working with low-resource languages, small amounts of symbolic lexical resources such as user-generated lexicons are often available even when gold-standard corpora are not. Such additional linguistic information is though often neglected, and recent neural approaches to cross-lingual tagging typically rely only on word and subword embeddings. While these representations are effective, our recent work has shown clear benefits of combining the best of both worlds: integrating conventional lexical information improves neural cross-lingual part-of-speech (PoS) tagging. However, little is known on how complementary such additional information is, and to what extent improvements depend on the coverage and quality of these external resources. This paper seeks to fill this gap by providing the first thorough analysis on the contributions of lexical resources for cross-lingual PoS tagging in neural times.

Includes members:
  Member "a survey of cross lingual word embedding models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multilingual alignment of contextual word representations")
  Member "morphosyntactic tagging with a meta bilstm model over context sensitive token encodings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "word embedding algorithms as generalized low rank models and their canonical form")
  Member "distant supervision from disparate sources for low resource part of speech tagging":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep contextualized self training for low resource dependency parsing")
  Member "on the practical computational power of finite precision rnns for language recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "does syntax need to grow on trees sources of hierarchical inductive bias in sequence to sequence networks")
  Member "evaluating the utility of hand crafted features in sequence labelling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "how to best use syntax in semantic role labelling")
  Member "what you can cram into a single vector probing sentence embeddings for linguistic properties":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word models")
  Member "improved neural machine translation with a syntax aware encoder and decoder":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")
  Member "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "exploiting language instructions for interpretable and compositional reinforcement learning")

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)