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: "morpho syntactic lexicon generation using graph based semi supervised learning"
  Abstract: Morpho-syntactic lexicons provide information about the morphological and syntactic roles of words in a language. Such lexicons are not available for all languages and even when available, their coverage can be limited. We present a graph-based semi-supervised learning method that uses the morphological, syntactic and semantic relations between words to automatically construct wide coverage lexicons from small seed sets. Our method is language-independent, and we show that we can expand a 1000 word seed lexicon to more than 100 times its size with high quality for 11 languages. In addition, the automatically created lexicons provide features that improve performance in two downstream tasks: morphological tagging and dependency parsing.

Connected through hyperedge HE-1 (induced by source paper "joint semantic synthesis and morphological analysis of the derived word"):
  with members:
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "multilingual language processing from bytes"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "character aware neural language models"
    - "an unsupervised method for uncovering morphological chains"
    - "segmental recurrent neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "mimicking word embeddings using subword rnns"):
  with members:
    - "morphological priors for probabilistic neural word embeddings"
    - "enriching word vectors with subword information"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "charagram embedding words and sentences via character n grams"
    - "character aware neural language models"
    - "compositional morphology for word representations and language modelling"
    - "polyglot distributed word representations for multilingual nlp"
    - "a universal part of speech tagset"

Connected through hyperedge HE-3 (induced by source paper "unsupervised learning of morphological forests"):
  with members:
    - "an unsupervised method for uncovering morphological chains"
    - "adam a method for stochastic optimization"
    - "polyglot distributed word representations for multilingual nlp"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "cross lingual morphological tagging for low resource languages"):
  with members:
    - "distributed representations of words and phrases and their compositionality"
    - "a universal part of speech tagset"

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)