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: "conll sigmorphon 2017 shared task universal morphological reinflection in 52 languages"
  Abstract: The CoNLL-SIGMORPHON 2017 shared task on supervised morphological generation required systems to be trained and tested in each of 52 typologically diverse languages. In sub-task 1, submitted systems were asked to predict a specific inflected form of a given lemma. In sub-task 2, systems were given a lemma and some of its specific inflected forms, and asked to complete the inflectional paradigm by predicting all of the remaining inflected forms. Both sub-tasks included high, medium, and low-resource conditions. Sub-task 1 received 24 system submissions, while sub-task 2 received 3 system submissions. Following the success of neural sequence-to-sequence models in the SIGMORPHON 2016 shared task, all but one of the submissions included a neural component. The results show that high performance can be achieved with small training datasets, so long as models have appropriate inductive bias or make use of additional unlabeled data or synthetic data. However, different biasing and data augmentation resulted in disjoint sets of inflected forms being predicted correctly, suggesting that there is room for future improvement.

Connected through hyperedge HE-1 (induced by source paper "pushing the limits of low resource morphological inflection"):
  with members:
    - "automatic keyboard layout design for low resource latin script languages"
    - "exact hard monotonic attention for character level transduction"
    - "rapid adaptation of neural machine translation to new languages"
    - "leveraging translations for speech transcription in low resource settings"
    - "unsupervised machine translation using monolingual corpora only"
    - "one shot neural cross lingual transfer for paradigm completion"
    - "attention is all you need"
    - "controllable invariance through adversarial feature learning"

Connected through hyperedge HE-2 (induced by source paper "universal lemmatizer a sequence to sequence model for lemmatizing universal dependencies treebanks"):
  with members:
    - "universal dependency parsing from scratch"
    - "marrying universal dependencies and universal morphology"
    - "su rug at the conll sigmorphon 2017 shared task morphological inflection with attentional sequence to sequence models"
    - "opennmt open source toolkit for neural machine translation"
    - "unlabeled data for morphological generation with character based sequence to sequence models"
    - "deep biaffine attention for neural dependency parsing"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-3 (induced by source paper "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"):
  with members:
    - "past present future a computational investigation of the typology of tense in 1000 languages"
    - "su rug at the conll sigmorphon 2017 shared task morphological inflection with attentional sequence to sequence models"
    - "learning language representations for typology prediction"
    - "probabilistic typology deep generative models of vowel inventories"
    - "encoding of phonology in a recurrent neural model of grounded speech"
    - "massively multilingual neural grapheme to phoneme conversion"
    - "tracking typological traits of uralic languages in distributed language representations"
    - "continuous multilinguality with language vectors"

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)