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: "cross lingual syntactic transfer with limited resources"
  Abstract: We describe a simple but effective method for cross-lingual syntactic transfer of dependency parsers, in the scenario where a large amount of translation data is not available.The method makes use of three steps: 1) a method for deriving cross-lingual word clusters, that can then be used in a multilingual parser; 2) a method for transferring lexical information from a target language to source language treebanks; 3) a method for integrating these steps with the density-driven annotation projection method of Rasooli and Collins(2015). Experiments show improvements over the state-of-the-art in several languages used in previous work (Rasooli and Collins, 2015;Zhang and Barzilay, 2015; Ammar et al.,2016), in a setting where the only source of translation data is the Bible, a considerably smaller corpus than the Europarl corpus used in previous work. Results using the Europarl corpus as a source of translation data show additional improvements over the results of Rasooli and Collins (2015). We conclude with results on 38 datasets (26 languages) from the Universal Dependencies corpora: 13 datasets(10 languages) have unlabeled attachment ac-curacies of 80% or higher; the average unlabeled accuracy on the 38 datasets is 74.8%.

Connected through hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity"):
  with members:
    - "multilingual alignment of contextual word representations"
    - "emerging cross lingual structure in pretrained language models"
    - "an empirical study on post processing methods for word embeddings"
    - "informing unsupervised pretraining with external linguistic knowledge"
    - "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"
    - "word similarity datasets for thai construction and evaluation"
    - "paws paraphrase adversaries from word scrambling"
    - "cross lingual ability of multilingual bert an empirical study"

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 "target language aware constrained inference for cross lingual dependency parsing"):
  with members:
    - "how multilingual is multilingual bert"
    - "low resource syntactic transfer with unsupervised source reordering"
    - "dependency grammar induction with a neural variational transition based parser"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"
    - "attention is all you need"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "word translation without parallel data"

Connected through hyperedge HE-4 (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"
    - "82 treebanks 34 models universal dependency parsing with multi treebank models"
    - "universal language model fine tuning for text classification"
    - "data augmentation for low resource neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "modeling named entity embedding distribution into hypersphere"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "lexicon infused phrase embeddings for named entity resolution"
    - "one billion word benchmark for measuring progress in statistical language modeling"
    - "cross lingual pseudo projected expectation regularization for weakly supervised learning"

Connected through hyperedge HE-6 (induced by source paper "low resource syntactic transfer with unsupervised source reordering"):
  with members:
    - "the galactic dependencies treebanks getting more data by synthesizing new languages"
    - "neural machine translation for low resource languages"
    - "fine grained prediction of syntactic typology discovering latent structure with supervised learning"
    - "a survey of word reordering in statistical machine translation computational models and language phenomena"
    - "deep biaffine attention for neural dependency parsing"
    - "many languages one parser"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "cross lingual dependency parsing using code mixed treebank"):
  with members:
    - "low resource syntactic transfer with unsupervised source reordering"
    - "parameter sharing between dependency parsers for related languages"
    - "design challenges and misconceptions in neural sequence labeling"
    - "cross lingual dependency parsing with late decoding for truly low resource languages"
    - "universal dependencies parsing for colloquial singaporean english"
    - "deep biaffine attention for neural dependency parsing"
    - "many languages one parser"

Connected through hyperedge HE-8 (induced by source paper "the best of both worlds lexical resources to improve low resource part of speech tagging"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "morphosyntactic tagging with a meta bilstm model over context sensitive token encodings"
    - "distant supervision from disparate sources for low resource part of speech tagging"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "evaluating the utility of hand crafted features in sequence labelling"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"

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)