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: "unsupervised disambiguation of syncretism in inflected lexicons"
  Abstract: Lexical ambiguity makes it difficult to compute various useful statistics of a corpus. A given word form might represent any of several morphological feature bundles. One can, however, use unsupervised learning (as in EM) to fit a model that probabilistically disambiguates word forms. We present such an approach, which employs a neural network to smoothly model a prior distribution over feature bundles (even rare ones). Although this basic model does not consider a token's context, that very property allows it to operate on a simple list of unigram type counts, partitioning each count among different analyses of that unigram. We discuss evaluation metrics for this novel task and report results on 5 languages.

Connected through hyperedge HE-1 (induced by source paper "morphological irregularity correlates with frequency"):
  with members:
    - "exact hard monotonic attention for character level transduction"
    - "on the complexity and typology of inflectional morphological systems"
    - "unimorph 2 0 universal morphology"

Connected through hyperedge HE-2 (induced by source paper "the conll sigmorphon 2018 shared task universal morphological reinflection"):
  with members:
    - "unimorph 2 0 universal morphology"
    - "marrying universal dependencies and universal morphology"
    - "finding the way from a to a sub character morphological inflection for the sigmorphon 2018 shared task"
    - "copenhagen at conll sigmorphon 2018 multilingual inflection in context with explicit morphosyntactic decoding"
    - "a structured variational autoencoder for contextual morphological inflection"
    - "attention is all you need"
    - "align and copy uzh at sigmorphon 2017 shared task for morphological reinflection"
    - "get to the point summarization with pointer generator networks"

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)