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: "polyglot distributed word representations for multilingual nlp"
  Abstract: Distributed word representations (word embeddings) have recently contributed to competitive performance in language modeling and several NLP tasks. In this work, we train word embeddings for more than 100 languages using their corresponding Wikipedias. We quantitatively demonstrate the utility of our word embeddings by using them as the sole features for training a part of speech tagger for a subset of these languages. We find their performance to be competitive with near state-of-art methods in English, Danish and Swedish. Moreover, we investigate the semantic features captured by these embeddings through the proximity of word groupings. We will release these embeddings publicly to help researchers in the development and enhancement of multilingual applications.

Connected through hyperedge HE-1 (induced by source paper "multilingual alignment of contextual word representations"):
  with members:
    - "cross lingual bert transformation for zero shot dependency parsing"
    - "a survey of cross lingual word embedding models"
    - "how multilingual is multilingual bert"
    - "context aware cross lingual mapping"
    - "simple and effective paraphrastic similarity from parallel translations"
    - "cross lingual language model pretraining"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "xnli evaluating cross lingual sentence representations"

Connected through hyperedge HE-2 (induced by source paper "using interlinear glosses as pivot in low resource multilingual machine translation"):
  with members:
    - "morphological word embeddings"
    - "character aware decoder for translation into morphologically rich languages"
    - "massively parallel cross lingual learning in low resource target language translation"
    - "improving character based decoding using target side morphological information for neural machine translation"
    - "opennmt open source toolkit for neural machine translation"
    - "six challenges for neural machine translation"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "polyglot neural language models a case study in cross lingual phonetic representation learning"

Connected through hyperedge HE-3 (induced by source paper "neural cross lingual transfer and limited annotated data for named entity recognition in danish"):
  with members:
    - "multilingual ner transfer for low resource languages"
    - "word translation without parallel data"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "natural language processing almost from scratch"
    - "introduction to the conll 2003 shared task language independent named entity recognition"
    - "tnt a statistical part of speech tagger"

Connected through hyperedge HE-4 (induced by source paper "charting the landscape of online cryptocurrency manipulation"):
  with members:
    - "data mining for detecting bitcoin ponzi schemes"
    - "social fingerprinting detection of spambot groups through dna inspired behavioral modeling"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"

Connected through hyperedge HE-5 (induced by source paper "representation learning for words and entities"):
  with members:
    - "deep multi view learning to rank"
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "deep contextualized word representations"
    - "deep sets"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "representation learning of entities and documents from knowledge base descriptions"
    - "new embedded representations and evaluation protocols for inferring transitive relations"
    - "neural collaborative filtering"

Connected through hyperedge HE-6 (induced by source paper "hierarchically refined label attention network for sequence labeling"):
  with members:
    - "benchmarking approximate inference methods for neural structured prediction"
    - "design challenges and misconceptions in neural sequence labeling"
    - "two local models for neural constituent parsing"
    - "joint embedding of words and labels for text classification"
    - "semi supervised sequence modeling with cross view training"
    - "cross target stance classification with self attention networks"
    - "ncrf an open source neural sequence labeling toolkit"
    - "learning approximate inference networks for structured prediction"

Connected through hyperedge HE-7 (induced by source paper "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "learning cross lingual word embeddings from twitter via distant supervision"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "why is unsupervised alignment of english embeddings from different algorithms so hard"
    - "the limitations of cross language word embeddings evaluation"
    - "xnli evaluating cross lingual sentence representations"
    - "unsupervised multilingual word embeddings"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"

Connected through hyperedge HE-8 (induced by source paper "a neural entity coreference resolution review"):
  with members:
    - "context in neural machine translation a review of models and evaluations"
    - "incorporating context and external knowledge for pronoun coreference resolution"
    - "gendered ambiguous pronouns shared task boosting model confidence by evidence pooling"
    - "knowledge aware pronoun coreference resolution"
    - "measuring bias in contextualized word representations"
    - "improving generalization in coreference resolution via adversarial training"
    - "look again at the syntax relational graph convolutional network for gendered ambiguous pronoun resolution"
    - "gender bias in contextualized word embeddings"

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)