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: "probabilistic typology deep generative models of vowel inventories"
  Abstract: Linguistic typology studies the range of structures present in human language. The main goal of the field is to discover which sets of possible phenomena are universal, and which are merely frequent. For example, all languages have vowels, while most---but not all---languages have an /u/ sound. In this paper we present the first probabilistic treatment of a basic question in phonological typology: What makes a natural vowel inventory? We introduce a series of deep stochastic point processes, and contrast them with previous computational, simulation-based approaches. We provide a comprehensive suite of experiments on over 200 distinct languages.

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"
    - "tracking typological traits of uralic languages in distributed language representations"
    - "continuous multilinguality with language vectors"

Connected through hyperedge HE-3 (induced by source paper "what do language representations really represent"):
  with members:
    - "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"
    - "found in translation reconstructing phylogenetic language trees from translations"
    - "learning language representations for typology prediction"
    - "continuous multilinguality with language vectors"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "many languages one parser"
    - "grammar as a foreign language"

Connected through hyperedge HE-4 (induced by source paper "modeling language variation and universals a survey on typological linguistics for natural language processing"):
  with members:
    - "morphological word embeddings"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "xnli evaluating cross lingual sentence representations"
    - "a deep generative model of vowel formant typology"
    - "deep contextualized word representations"
    - "deep probabilistic logic a unifying framework for indirect supervision"
    - "adversarial propagation and zero shot cross lingual transfer of word vector specialization"
    - "past present future a computational investigation of the typology of tense in 1000 languages"

Connected through hyperedge HE-5 (induced by source paper "deepmove learning place representations through large scale movement data"):
  with members:
    - "the interpretation of dream meaning resolving ambiguity using latent semantic analysis in a small corpus of text"
    - "node2vec scalable feature learning for networks"
    - "deepcity a feature learning framework for mining location check ins"
    - "revisiting semi supervised learning with graph embeddings"
    - "neural architectures for named entity recognition"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-6 (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"
    - "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"

Connected through hyperedge HE-7 (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"
    - "massively multilingual neural grapheme to phoneme conversion"
    - "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)