human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "lidioms a multilingual linked idioms data set"
  Abstract: In this paper, we describe the LIDIOMS data set, a multilingual RDF representation of idioms currently containing five languages: English, German, Italian, Portuguese, and Russian. The data set is intended to support natural language processing applications by providing links between idioms across languages. The underlying data was crawled and integrated from various sources. To ensure the quality of the crawled data, all idioms were evaluated by at least two native speakers. Herein, we present the model devised for structuring the data. We also provide the details of linking LIDIOMS to well-known multilingual data sets such as BabelNet. The resulting data set complies with best practices according to Linguistic Linked Open Data Community.

Connected through hyperedge HE-1 (induced by source paper "semantic web for machine translation challenges and directions"):
  with members:
    - "augmenting neural machine translation with knowledge graphs"
    - "machine translation using semantic web technologies a survey"
    - "mag a multilingual knowledge base agnostic and deterministic entity linking approach"
    - "six challenges for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "machine translation using semantic web technologies a survey"):
  with members:
    - "mag a multilingual knowledge base agnostic and deterministic entity linking approach"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "towards an arabic english machine translation based on semantic web"
    - "six challenges for neural machine translation"
    - "a survey of word reordering in statistical machine translation computational models and language phenomena"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "multilingual knowledge graph embeddings for cross lingual knowledge alignment"
    - "word2vec vs dbnary augmenting meteor using vector representations or lexical resources"

Question: Please tell me which class the center node 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)