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 discovery of morphologically related words based on orthographic and semantic similarity"
  Abstract: We present an algorithm that takes an unannotated corpus as its input, and returns a ranked list of probable morphologically related pairs as its output. The algorithm tries to discover morphologically related pairs by looking for pairs that are both orthographically and semantically similar, where orthographic similarity is measured in terms of minimum edit distance, and semantic similarity is measured in terms of mutual information. The procedure does not rely on a morpheme concatenation model, nor on distributional properties of word substrings (such as affix frequency). Experiments with German and English input give encouraging results, both in terms of precision (proportion of good pairs found at various cutoff points of the ranked list), and in terms of a qualitative analysis of the types of morphological patterns discovered by the algorithm.

Connected through hyperedge HE-1 (induced by source paper "morse semantic ally drive n morpheme segment er"):
  with members:
    - "an unsupervised method for uncovering morphological chains"
    - "efficient estimation of word representations in vector space"
    - "unsupervised discovery of morphemes"

Connected through hyperedge HE-2 (induced by source paper "morphological analysis for the maltese language the challenges of a hybrid system"):
  with members:
    - "an unsupervised method for uncovering morphological chains"

Connected through hyperedge HE-3 (induced by source paper "probabilistic modelling of morphologically rich languages"):
  with members:
    - "compositional morphology for word representations and language modelling"
    - "one billion word benchmark for measuring progress in statistical language modeling"
    - "efficient estimation of word representations in vector space"
    - "a fast and simple algorithm for training neural probabilistic language models"
    - "on the difficulty of training recurrent neural networks"
    - "from frequency to meaning vector space models of semantics"
    - "empirical methods for compound splitting"
    - "classes for fast maximum entropy training"

Connected through hyperedge HE-4 (induced by source paper "an unsupervised method for uncovering morphological chains"):
  with members:
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "acquisition of morphological families and derivational series from a machine readable dictionary"):
  with members:
    - "unsupervised learning of morphology without morphemes"
    - "unsupervised discovery of morphemes"

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)