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: "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
  Abstract: A core part of linguistic typology is the classification of languages according to linguistic properties, such as those detailed in the World Atlas of Language Structure (WALS). Doing this manually is prohibitively time-consuming, which is in part evidenced by the fact that only 100 out of over 7,000 languages spoken in the world are fully covered in WALS. #R##N#We learn distributed language representations, which can be used to predict typological properties on a massively multilingual scale. Additionally, quantitative and qualitative analyses of these language embeddings can tell us how language similarities are encoded in NLP models for tasks at different typological levels. The representations are learned in an unsupervised manner alongside tasks at three typological levels: phonology (grapheme-to-phoneme prediction, and phoneme reconstruction), morphology (morphological inflection), and syntax (part-of-speech tagging). #R##N#We consider more than 800 languages and find significant differences in the language representations encoded, depending on the target task. For instance, although Norwegian Bokm{\aa}l and Danish are typologically close to one another, they are phonologically distant, which is reflected in their language embeddings growing relatively distant in a phonological task. We are also able to predict typological features in WALS with high accuracies, even for unseen language families.

Connected through hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity"):
  with members:
    - "multilingual alignment of contextual word representations"
    - "emerging cross lingual structure in pretrained language models"
    - "an empirical study on post processing methods for word embeddings"
    - "informing unsupervised pretraining with external linguistic knowledge"
    - "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"
    - "word similarity datasets for thai construction and evaluation"
    - "paws paraphrase adversaries from word scrambling"
    - "cross lingual ability of multilingual bert an empirical study"

Connected through hyperedge HE-2 (induced by source paper "parameter space factorization for zero shot learning across tasks and languages"):
  with members:
    - "the case for bayesian deep learning"
    - "learning and evaluating general linguistic intelligence"
    - "what do language representations really represent"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "mlqa evaluating cross lingual extractive question answering"
    - "how multilingual is multilingual bert"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "cross lingual language model pretraining"

Connected through hyperedge HE-3 (induced by source paper "zero shot cross lingual transfer with meta learning"):
  with members:
    - "domain adaptive dialog generation via meta learning"
    - "x wikire a large multilingual resource for relation extraction as machine comprehension"
    - "investigating meta learning algorithms for low resource natural language understanding tasks"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "on first order meta learning algorithms"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "xnli evaluating cross lingual sentence representations"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-4 (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"
    - "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"
    - "learning language representations for typology prediction"

Connected through hyperedge HE-5 (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"
    - "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"
    - "tracking typological traits of uralic languages in distributed language representations"
    - "continuous multilinguality with language vectors"

Connected through hyperedge HE-6 (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"
    - "found in translation reconstructing phylogenetic language trees from translations"
    - "learning language representations for typology prediction"
    - "probabilistic typology deep generative models of vowel inventories"
    - "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-7 (induced by source paper "modeling language variation and universals a survey on typological linguistics for natural language processing"):
  with members:
    - "morphological word 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"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "multitask and multilingual modelling for lexical analysis"):
  with members:
    - "parameter sharing between dependency parsers for related languages"
    - "tracking typological traits of uralic languages in distributed language representations"
    - "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"

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)