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: "massively multilingual neural grapheme to phoneme conversion"
  Abstract: Grapheme-to-phoneme conversion (g2p) is necessary for text-to-speech and automatic speech recognition systems. Most g2p systems are monolingual: they require language-specific data or handcrafting of rules. Such systems are difficult to extend to low resource languages, for which data and handcrafted rules are not available. As an alternative, we present a neural sequence-to-sequence approach to g2p which is trained on spelling--pronunciation pairs in hundreds of languages. The system shares a single encoder and decoder across all languages, allowing it to utilize the intrinsic similarities between different writing systems. We show an 11% improvement in phoneme error rate over an approach based on adapting high-resource monolingual g2p models to low-resource languages. Our model is also much more compact relative to previous approaches.

Connected through hyperedge HE-1 (induced by source paper "sparse sequence to sequence models"):
  with members:
    - "attention is not explanation"
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "sparse and constrained attention for neural machine translation"
    - "hard non monotonic attention for character level transduction"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "classical structured prediction losses for sequence to sequence learning"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"

Connected through hyperedge HE-2 (induced by source paper "few shot and zero shot learning for historical text normalization"):
  with members:
    - "a large scale comparison of historical text normalization systems"
    - "tied multitask learning for neural speech translation"
    - "xnmt the extensible neural machine translation toolkit"
    - "an evaluation of neural machine translation models on historical spelling normalization"
    - "evaluating historical text normalization systems how well do they generalize"
    - "unimorph 2 0 universal morphology"
    - "scheduled multi task learning from syntax to translation"
    - "one shot neural cross lingual transfer for paradigm completion"

Connected through hyperedge HE-3 (induced by source paper "lessons learned in multilingual grounded language learning"):
  with members:
    - "vision as an interlingua learning multilingual semantic embeddings of untranscribed speech"
    - "imagination improves multimodal translation"
    - "learning visually grounded sentence representations"
    - "multilingual multi modal embeddings for natural language processing"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning language representations for typology prediction"
    - "attention strategies for multi source sequence to sequence learning"

Connected through hyperedge HE-4 (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"
    - "conll sigmorphon 2017 shared task universal morphological reinflection in 52 languages"
    - "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"
    - "tracking typological traits of uralic languages in distributed language representations"
    - "continuous multilinguality with language vectors"

Connected through hyperedge HE-5 (induced by source paper "tracking typological traits of uralic languages in distributed language representations"):
  with members:
    - "past present future a computational investigation of the typology of tense in 1000 languages"
    - "learning language representations for typology prediction"
    - "probabilistic typology deep generative models of vowel inventories"
    - "continuous multilinguality with language vectors"
    - "polyglot neural language models a case study in cross lingual phonetic representation learning"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "adam a method for stochastic optimization"

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)