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: "morphological segmentation inside out"
  Abstract: Morphological segmentation has traditionally been modeled with non-hierarchical models, which yield flat segmentations as output. In many cases, however, proper morphological analysis requires hierarchical structure -- especially in the case of derivational morphology. In this work, we introduce a discriminative, joint model of morphological segmentation along with the orthographic changes that occur during word formation. To the best of our knowledge, this is the first attempt to approach discriminative segmentation with a context-free model. Additionally, we release an annotated treebank of 7454 English words with constituency parses, encouraging future research in this area.

Connected through hyperedge HE-1 (induced by source paper "stem driven language models for morphologically rich languages"):
  with members:
    - "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"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-2 (induced by source paper "neural factor graph models for cross lingual morphological tagging"):
  with members:
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "word representation models for morphologically rich languages in neural machine translation"
    - "cross lingual morphological tagging for low resource languages"
    - "neural morphological tagging from characters for morphologically rich languages"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "bidirectional lstm crf models for sequence tagging"
    - "loopy belief propagation for approximate inference an empirical study"

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)