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: "huge automatically extracted training sets for multilingual word sense disambiguation"
  Abstract: We release to the community six large-scale sense-annotated datasets in multiple language to pave the way for supervised multilingual Word Sense Disambiguation. Our datasets cover all the nouns in the English WordNet and their translations in other languages for a total of millions of sense-tagged sentences. Experiments prove that these corpora can be effectively used as training sets for supervised WSD systems, surpassing the state of the art for low-resourced languages and providing competitive results for English, where manually annotated training sets are accessible. The data is available at trainomatic.org.

Connected through hyperedge HE-1 (induced by source paper "a short survey on sense annotated corpora"):
  with members:
    - "language modelling makes sense propagating representations through wordnet for full coverage word sense disambiguation"
    - "glossbert bert for word sense disambiguation with gloss knowledge"
    - "incorporating glosses into neural word sense disambiguation"
    - "shotgunwsd an unsupervised algorithm for global word sense disambiguation inspired by dna sequencing"
    - "named entity disambiguation for noisy text"
    - "embedding words and senses together via joint knowledge enhanced training"
    - "semi supervised word sense disambiguation with neural models"
    - "word sense disambiguation using a bidirectional lstm"

Connected through hyperedge HE-2 (induced by source paper "a short survey on sense annotated corpora for diverse languages and resources"):
  with members:
    - "knowledge based word sense disambiguation using topic models"
    - "shotgunwsd an unsupervised algorithm for global word sense disambiguation inspired by dna sequencing"
    - "named entity disambiguation for noisy text"
    - "semi supervised word sense disambiguation with neural models"
    - "word sense disambiguation using a bidirectional lstm"

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)