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: "encoding prior knowledge with eigenword embeddings"
  Abstract: Canonical correlation analysis (CCA) is a method for reducing the dimension of data represented using two views. It has been previously used to derive word embeddings, where one view indicates a word, and the other view indicates its context. We describe a way to incorporate prior knowledge into CCA, give a theoretical justification for it, and test it by deriving word embeddings and evaluating them on a myriad of datasets.

Connected through hyperedge HE-1 (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-2 (induced by source paper "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"):
  with members:
    - "cross lingual models of word embeddings an empirical comparison"
    - "a network based end to end trainable task oriented dialogue system"
    - "a dual embedding space model for document ranking"
    - "simverb 3500 a large scale evaluation set of verb similarity"
    - "learning crosslingual word embeddings without bilingual corpora"
    - "charagram embedding words and sentences via character n grams"
    - "neural belief tracker data driven dialogue state tracking"
    - "many languages one parser"

Connected through hyperedge HE-3 (induced by source paper "survey on the use of typological information in natural language processing"):
  with members:
    - "polyglot neural language models a case study in cross lingual phonetic representation learning"
    - "classifying syntactic regularities for hundreds of languages"
    - "many languages one parser"
    - "separated by an un common language towards judgment language informed vector space modeling"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "bilingual distributed word representations from document aligned comparable data"
    - "multilingual models for compositional distributed semantics"
    - "reconstructing native language typology from foreign language usage"

Connected through hyperedge HE-4 (induced by source paper "canonical correlation inference for mapping abstract scenes to text"):
  with members:
    - "show and tell a neural image caption generator"

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)