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: "one model two languages training bilingual parsers with harmonized treebanks"
  Abstract: We introduce an approach to train lexicalized parsers using bilingual corpora obtained by merging harmonized treebanks of different languages, producing parsers that can analyze sentences in either of the learned languages, or even sentences that mix both. We test the approach on the Universal Dependency Treebanks, training with MaltParser and MaltOptimizer. The results show that these bilingual parsers are more than competitive, as most combinations not only preserve accuracy, but some even achieve significant improvements over the corresponding monolingual parsers. Preliminary experiments also show the approach to be promising on texts with code-switching and when more languages are added.

Connected through hyperedge HE-1 (induced by source paper "75 languages 1 model parsing universal dependencies universally"):
  with members:
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "evaluating contextualized embeddings on 54 languages in pos tagging lemmatization and dependency parsing"
    - "polyglot contextual representations improve crosslingual transfer"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "visualizing attention in transformer based language representation models"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "parameter sharing between dependency parsers for related languages"

Connected through hyperedge HE-2 (induced by source paper "low resource parsing with crosslingual contextualized representations"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "polyglot contextual representations improve crosslingual transfer"
    - "linguistic knowledge and transferability of contextual representations"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "parameter sharing between dependency parsers for related languages"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "parameter sharing between dependency parsers for related languages"):
  with members:
    - "transfer learning to learn with multitask neural model search"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "evolutionary architecture search for deep multitask networks"
    - "cross stitch networks for multi task learning"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"

Connected through hyperedge HE-4 (induced by source paper "a non projective greedy dependency parser with bidirectional lstms"):
  with members:
    - "universal unsupervised rule based uncovered sentiment analysis"
    - "a full non monotonic transition system for unrestricted non projective parsing"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "yara parser a fast and accurate dependency parser"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "universal unsupervised rule based uncovered sentiment analysis"):
  with members:
    - "a convolutional neural network for modelling sentences"
    - "a universal part of speech tagset"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-6 (induced by source paper "many languages one parser"):
  with members:
    - "massively multilingual word embeddings"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "bidirectional lstm crf models for sequence tagging"
    - "transition based dependency parsing with stack long short term memory"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep recurrent neural networks"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "universal unsupervised uncovered sentiment analysis"):
  with members:
    - "exploiting multi typed treebanks for parsing with deep multi task learning"
    - "many languages one parser"
    - "a convolutional neural network for modelling sentences"
    - "a universal part of speech tagset"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-8 (induced by source paper "challenges of computational processing of code switching"):
  with members:
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "part of speech tagging for code mixed indian social media text at icon 2015"
    - "syntactic and semantic features for code switching factored language models"
    - "a universal part of speech tagset"

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)