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: "an unsupervised method for uncovering morphological chains"
  Abstract: Most state-of-the-art systems today produce morphological analysis based only on orthographic patterns. In contrast, we propose a model for unsupervised morphological analysis that integrates orthographic and semantic views of words. We model word formation in terms of morphological chains, from base words to the observed words, breaking the chains into parent-child relations. We use log-linear models with morpheme and word-level features to predict possible parents, including their modifications, for each word. The limited set of candidate parents for each word render contrastive estimation feasible. Our model consistently matches or outperforms five state-of-the-art systems on Arabic, English and Turkish.

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"
    - "segmental recurrent neural networks"
    - "morpho syntactic lexicon generation using graph based semi supervised learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "unsupervised learning of morphological forests"):
  with members:
    - "morpho syntactic lexicon generation using graph based semi supervised learning"
    - "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-3 (induced by source paper "building morphological chains for agglutinative languages"):
  with members:
    - "a joint model for word embedding and word morphology"
    - "efficient estimation of word representations in vector space"
    - "unsupervised discovery of morphemes"

Connected through hyperedge HE-4 (induced by source paper "morse semantic ally drive n morpheme segment er"):
  with members:
    - "efficient estimation of word representations in vector space"
    - "unsupervised discovery of morphemes"
    - "unsupervised discovery of morphologically related words based on orthographic and semantic similarity"

Connected through hyperedge HE-5 (induced by source paper "a trie structured bayesian model for unsupervised morphological segmentation"):
  with members:
    - "efficient estimation of word representations in vector space"
    - "unsupervised discovery of morphemes"

Connected through hyperedge HE-6 (induced by source paper "morphological analysis for the maltese language the challenges of a hybrid system"):
  with members:
    - "unsupervised discovery of morphologically related words based on orthographic and semantic similarity"

Connected through hyperedge HE-7 (induced by source paper "a joint model for word embedding and word morphology"):
  with members:
    - "character level convolutional networks for text classification"
    - "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"
    - "adam a method for stochastic optimization"
    - "compositional morphology for word representations and language modelling"
    - "neural machine translation by jointly learning to align and translate"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "morphological inflection generation using character sequence to sequence learning"):
  with members:
    - "morphological word embeddings"
    - "lstm a search space odyssey"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "effective approaches to attention based neural machine translation"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "sequence to sequence neural net models for grapheme to phoneme conversion"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)