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 morphemes"
  Abstract: We present two methods for unsupervised segmentation of words into morpheme-like units. The model utilized is especially suited for languages with a rich morphology, such as Finnish. The first method is based on the Minimum Description Length (MDL) principle and works online. In the second method, Maximum Likelihood (ML) optimization is used. The quality of the segmentations is measured using an evaluation method that compares the segmentations produced to an existing morphological analysis. Experiments on both Finnish and English corpora show that the presented methods perform well compared to a current state-of-the-art system.

Connected through hyperedge HE-1 (induced by source paper "parsimonious morpheme segmentation with an application to enriching word embeddings"):
  with members:
    - "morphological word embeddings"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "enriching word vectors with subword information"
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "bag of tricks for efficient text classification"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "bidirectional lstm crf models for sequence tagging"
    - "character aware neural language models"

Connected through hyperedge HE-2 (induced by source paper "stem driven language models for morphologically rich languages"):
  with members:
    - "morphological segmentation inside out"
    - "compositional representation of morphologically rich input for neural machine translation"
    - "from characters to words to in between do we capture morphology"
    - "character word lstm language models"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "a joint model for word embedding and word morphology"
    - "word representation models for morphologically rich languages in neural machine translation"
    - "generalizing and hybridizing count based and neural language models"

Connected through hyperedge HE-3 (induced by source paper "investigation on n gram approximated rnnlms for recognition of morphologically rich speech"):
  with members:
    - "n gram language modeling using recurrent neural network estimation"
    - "automatic speech recognition with very large conversational finnish and estonian vocabularies"
    - "recurrent neural network regularization"
    - "entropy based pruning of backoff language models"

Connected through hyperedge HE-4 (induced by source paper "subword level language identification for intra word code switching"):
  with members:
    - "a fast compact accurate model for language identification of codemixed text"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "segmental recurrent neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "cognate aware morphological segmentation for multilingual neural translation"):
  with members:
    - "using global constraints and reranking to improve cognates detection"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-6 (induced by source paper "a characterwise windowed approach to hebrew morphological segmentation"):
  with members:
    - "universal word segmentation implementation and interpretation"
    - "neural word segmentation learning for chinese"
    - "tensorflow a system for large scale machine learning"
    - "polyglot ner massive multilingual named entity recognition"
    - "a universal part of speech tagset"

Connected through hyperedge HE-7 (induced by source paper "fortification of neural morphological segmentation models for polysynthetic minimal resource languages"):
  with members:
    - "polyglot neural language models a case study in cross lingual phonetic representation learning"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "many languages one parser"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "challenges of language technologies for the indigenous languages of the americas"):
  with members:
    - "emerging language spaces learned from massively multilingual corpora"
    - "from characters to words to in between do we capture morphology"
    - "survey on the use of typological information in natural language processing"

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)