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: "mag a multilingual knowledge base agnostic and deterministic entity linking approach"
  Abstract: Entity linking has recently been the subject of a significant body of research. Currently, the best performing approaches rely on trained mono-lingual models. Porting these approaches to other languages is consequently a difficult endeavor as it requires corresponding training data and retraining of the models. We address this drawback by presenting a novel multilingual, knowledge-base agnostic and deterministic approach to entity linking, dubbed MAG. MAG is based on a combination of context-based retrieval on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data sets and in 7 languages. Our results show that the best approach trained on English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse on datasets in other languages. MAG on the other hand achieves state-of-the-art performance on English datasets and reaches a micro F-measure that is up to 0.6 higher than that of PBOH on non-English languages.

Connected through hyperedge HE-1 (induced by source paper "augmenting neural machine translation with knowledge graphs"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "learning beyond datasets knowledge graph augmented neural networks for natural language processing"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "machine translation using semantic web technologies a survey"
    - "an evaluation of neural machine translation models on historical spelling normalization"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "learning word vectors for 157 languages"
    - "the importance of being recurrent for modeling hierarchical structure"

Connected through hyperedge HE-2 (induced by source paper "falcon 2 0 an entity and relation linking tool over wikidata"):
  with members:
    - "context aware entity linking with attentive neural networks on wikidata knowledge graph"
    - "opentapioca lightweight entity linking for wikidata"
    - "neural collective entity linking"
    - "end to end neural entity linking"
    - "deeptype multilingual entity linking by neural type system evolution"
    - "no one is perfect analysing the performance of question answering components over the dbpedia knowledge graph"
    - "deep joint entity disambiguation with local neural attention"
    - "tagme on the fly annotation of short text fragments by wikipedia entities"

Connected through hyperedge HE-3 (induced by source paper "semantic web for machine translation challenges and directions"):
  with members:
    - "augmenting neural machine translation with knowledge graphs"
    - "lidioms a multilingual linked idioms data set"
    - "machine translation using semantic web technologies a survey"
    - "six challenges for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "machine translation using semantic web technologies a survey"):
  with members:
    - "lidioms a multilingual linked idioms data set"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "entity linking in 40 languages using mag"):
  with members:
    - "bengal an automatic benchmark generator for entity recognition and linking"

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)