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: "tracking typological traits of uralic languages in distributed language representations"
  Abstract: Although linguistic typology has a long history, computational approaches have only recently gained popularity. The use of distributed representations in computational linguistics has also become increasingly popular. A recent development is to learn distributed representations of language, such that typologically similar languages are spatially close to one another. Although empirical successes have been shown for such language representations, they have not been subjected to much typological probing. In this paper, we first look at whether this type of language representations are empirically useful for model transfer between Uralic languages in deep neural networks. We then investigate which typological features are encoded in these representations by attempting to predict features in the World Atlas of Language Structures, at various stages of fine-tuning of the representations. We focus on Uralic languages, and find that some typological traits can be automatically inferred with accuracies well above a strong baseline.

Connected through hyperedge HE-1 (induced by source paper "uncovering probabilistic implications in typological knowledge bases"):
  with members:
    - "a probabilistic generative model of linguistic typology"
    - "semantic drift in multilingual representations"
    - "parameter sharing between dependency parsers for related languages"
    - "modeling language variation and universals a survey on typological linguistics for natural language processing"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "a deep generative model of vowel formant typology"
    - "past present future a computational investigation of the typology of tense in 1000 languages"
    - "the galactic dependencies treebanks getting more data by synthesizing new languages"

Connected through hyperedge HE-2 (induced by source paper "a probabilistic generative model of linguistic typology"):
  with members:
    - "what do language representations really represent"
    - "semantic drift in multilingual representations"
    - "modeling language variation and universals a survey on typological linguistics for natural language processing"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "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"

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"
    - "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"
    - "massively multilingual neural grapheme to phoneme conversion"
    - "continuous multilinguality with language vectors"

Connected through hyperedge HE-4 (induced by source paper "emerging language spaces learned from massively multilingual corpora"):
  with members:
    - "past present future a computational investigation of the typology of tense in 1000 languages"
    - "continuous multilinguality with language vectors"
    - "fully character level neural machine translation without explicit segmentation"
    - "multi source neural translation"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "multi task sequence to sequence learning"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "multitask and multilingual modelling for lexical analysis"):
  with members:
    - "parameter sharing between dependency parsers for related languages"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "the parallel meaning bank towards a multilingual corpus of translations annotated with compositional meaning representations"
    - "one model to rule them all multitask and multilingual modelling for lexical analysis"
    - "semantic tagging with deep residual networks"
    - "byte based language identification with deep convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "parser training with heterogeneous treebanks"):
  with members:
    - "continuous multilinguality with language vectors"
    - "domain control for neural machine translation"
    - "polyglot neural language models a case study in cross lingual phonetic representation learning"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "many languages one parser"

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)