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: "82 treebanks 34 models universal dependency parsing with multi treebank models"
  Abstract: We present the Uppsala system for the CoNLL 2018 Shared Task on universal dependency parsing. Our system is a pipeline consisting of three components: the first performs joint word and sentence segmentation; the second predicts part-of- speech tags and morphological features; the third predicts dependency trees from words and tags. Instead of training a single parsing model for each treebank, we trained models with multiple treebanks for one language or closely related languages, greatly reducing the number of models. On the official test run, we ranked 7th of 27 teams for the LAS and MLAS metrics. Our system obtained the best scores overall for word segmentation, universal POS tagging, and morphological features.

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 "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"):
  with members:
    - "context aware cross lingual mapping"
    - "linguistic knowledge and transferability of contextual representations"
    - "gromov wasserstein alignment of word embedding spaces"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "parameter sharing between dependency parsers for related languages"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (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-4 (induced by source paper "multilingual is not enough bert for finnish"):
  with members:
    - "a finnish news corpus for named entity recognition"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "how multilingual is multilingual bert"
    - "cross lingual language model pretraining"
    - "roberta a robustly optimized bert pretraining approach"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-5 (induced by source paper "deep contextualized word embeddings in transition based and graph based dependency parsing a tale of two parsers revisited"):
  with members:
    - "assessing bert s syntactic abilities"
    - "universal dependency parsing from scratch"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "the non utility of structural features in bilstm based dependency parsers"
    - "75 languages 1 model parsing universal dependencies universally"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "what should do can lstms learn when parsing auxiliary verb constructions"):
  with members:
    - "recursive subtree composition in lstm based dependency parsing"
    - "studying the inductive biases of rnns with synthetic variations of natural languages"
    - "can lstm learn to capture agreement the case of basque"
    - "deep contextualized word representations"
    - "analysis methods in neural language processing a survey"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep rnns encode soft hierarchical syntax"
    - "colorless green recurrent networks dream hierarchically"

Connected through hyperedge HE-7 (induced by source paper "a systematic comparison of methods for low resource dependency parsing on genuinely low resource languages"):
  with members:
    - "data augmentation via dependency tree morphing for low resource languages"
    - "parameter sharing between dependency parsers for related languages"
    - "parser training with heterogeneous treebanks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "data augmentation for low resource neural machine translation"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"

Connected through hyperedge HE-8 (induced by source paper "cross lingual parsing with polyglot training and multi treebank learning a faroese case study"):
  with members:
    - "polyglot contextual representations improve crosslingual transfer"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "distant supervision from disparate sources for low resource part of speech tagging"
    - "parser training with heterogeneous treebanks"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "cross lingual dependency parsing with late decoding for truly low resource languages"

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)