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: "survey on the use of typological information in natural language processing"
  Abstract: In recent years linguistic typology, which classifies the world's languages according to their functional and structural properties, has been widely used to support multilingual NLP. While the growing importance of typological information in supporting multilingual tasks has been recognised, no systematic survey of existing typological resources and their use in NLP has been published. This paper provides such a survey as well as discussion which we hope will both inform and inspire future work in the area.

Connected through hyperedge HE-1 (induced by source paper "lost in embedding space explaining cross lingual task performance with eigenvalue divergence"):
  with members:
    - "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"
    - "a survey of cross lingual word embedding models"
    - "what do language representations really represent"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "bilingual lexicon induction with semi supervision in non isometric embedding spaces"
    - "investigating multilingual nmt representations at scale"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "do we really need fully unsupervised cross lingual embeddings"

Connected through hyperedge HE-2 (induced by source paper "choosing transfer languages for cross lingual learning"):
  with members:
    - "xnmt the extensible neural machine translation toolkit"
    - "distant supervision from disparate sources for low resource part of speech tagging"
    - "neural factor graph models for cross lingual morphological tagging"
    - "near or far wide range zero shot cross lingual dependency parsing"
    - "zero shot neural transfer for cross lingual entity linking"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "when and why are pre trained word embeddings useful for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "working hard or hardly working challenges of integrating typology into neural dependency parsers"):
  with members:
    - "low resource syntactic transfer with unsupervised source reordering"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"
    - "the galactic dependencies treebanks getting more data by synthesizing new languages"
    - "fine grained prediction of syntactic typology discovering latent structure with supervised learning"
    - "deep biaffine attention for neural dependency parsing"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "many languages one parser"

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 "challenges of language technologies for the indigenous languages of the americas"):
  with members:
    - "emerging language spaces learned from massively multilingual corpora"
    - "from characters to words to in between do we capture morphology"
    - "unsupervised discovery of morphemes"

Connected through hyperedge HE-6 (induced by source paper "learning language representations for typology prediction"):
  with members:
    - "learning to generate reviews and discovering sentiment"
    - "what do neural machine translation models learn about morphology"
    - "continuous multilinguality with language vectors"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "polyglot neural language models a case study in cross lingual phonetic representation learning"
    - "classifying syntactic regularities for hundreds of languages"
    - "what do recurrent neural network grammars learn about syntax"
    - "google s multilingual neural machine translation system enabling zero shot translation"

Connected through hyperedge HE-7 (induced by source paper "fine grained prediction of syntactic typology discovering latent structure with supervised learning"):
  with members:
    - "the galactic dependencies treebanks getting more data by synthesizing new languages"
    - "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)