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: "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
  Abstract: This paper describes our system (HIT-SCIR) submitted to the CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies. We base our submission on Stanford's winning system for the CoNLL 2017 shared task and make two effective extensions: 1) incorporating deep contextualized word embeddings into both the part of speech tagger and parser; 2) ensembling parsers trained with different initialization. We also explore different ways of concatenating treebanks for further improvements. Experimental results on the development data show the effectiveness of our methods. In the final evaluation, our system was ranked first according to LAS (75.84%) and outperformed the other systems by a large margin.

Connected through hyperedge HE-1 (induced by source paper "parsing as pretraining"):
  with members:
    - "assessing bert s syntactic abilities"
    - "viable dependency parsing as sequence labeling"
    - "better faster stronger sequence tagging constituent parsers"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "designing and interpreting probes with control tasks"
    - "the non utility of structural features in bilstm based dependency parsers"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "linguistic knowledge and transferability of contextual representations"

Connected through hyperedge HE-2 (induced by source paper "a hybrid approach to dependency parsing combining rules and morphology with deep learning"):
  with members:
    - "what do character level models learn about morphology the case of dependency parsing"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "transition based dependency parsing with stack long short term memory"

Connected through hyperedge HE-3 (induced by source paper "linspector multilingual probing tasks for word representations"):
  with members:
    - "linspector web a multilingual probing suite for word representations"
    - "a survey of cross lingual word embedding models"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "xnli evaluating cross lingual sentence representations"
    - "on the complexity and typology of inflectional morphological systems"
    - "allennlp a deep semantic natural language processing platform"
    - "unimorph 2 0 universal morphology"
    - "deep contextualized word representations"

Connected through hyperedge HE-4 (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"
    - "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"
    - "when and why are pre trained word embeddings useful for neural machine translation"

Connected through hyperedge HE-5 (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"
    - "parameter sharing between dependency parsers for related languages"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"

Connected through hyperedge HE-6 (induced by source paper "deep contextualized self training for low resource dependency parsing"):
  with members:
    - "viable dependency parsing as sequence labeling"
    - "a survey on recent advances in named entity recognition from deep learning models"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "no training required exploring random encoders for sentence classification"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "distant supervision from disparate sources for low resource part of speech tagging"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"
    - "deep contextualized word representations"

Connected through hyperedge HE-7 (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-8 (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"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

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)