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: "a deep generative model of vowel formant typology"
  Abstract: What makes some types of languages more probable than others? For instance, we know that almost all spoken languages contain the vowel phoneme /i/; why should that be? The field of linguistic typology seeks to answer these questions and, thereby, divine the mechanisms that underlie human language. In our work, we tackle the problem of vowel system typology, i.e., we propose a generative probability model of which vowels a language contains. In contrast to previous work, we work directly with the acoustic information -- the first two formant values -- rather than modeling discrete sets of phonemic symbols (IPA). We develop a novel generative probability model and report results based on a corpus of 233 languages.

Connected through hyperedge HE-1 (induced by source paper "sparsity emerges naturally in neural language models"):
  with members:
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
    - "understanding learning dynamics of language models with svcca"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "learning to generate reviews and discovering sentiment"
    - "exploring sparsity in recurrent neural networks"
    - "opening the black box of deep neural networks via information"
    - "spine sparse interpretable neural embeddings"
    - "representation of linguistic form and function in recurrent neural networks"

Connected through hyperedge HE-2 (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"
    - "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"
    - "learning language representations for typology prediction"

Connected through hyperedge HE-3 (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"
    - "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"
    - "unsupervised neural machine translation"

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)