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 ner massive multilingual named entity recognition"
  Abstract: The increasing diversity of languages used on the web introduces a new level of complexity to Information Retrieval (IR) systems. We can no longer assume that textual content is written in one language or even the same language family. In this paper, we demonstrate how to build massive multilingual annotators with minimal human expertise and intervention. We describe a system that builds Named Entity Recognition (NER) annotators for 40 major languages using Wikipedia and Freebase. Our approach does not require NER human annotated datasets or language specific resources like treebanks, parallel corpora, and orthographic rules. The novelty of approach lies therein - using only language agnostic techniques, while achieving competitive performance. #R##N#Our method learns distributed word representations (word embeddings) which encode semantic and syntactic features of words in each language. Then, we automatically generate datasets from Wikipedia link structure and Freebase attributes. Finally, we apply two preprocessing stages (oversampling and exact surface form matching) which do not require any linguistic expertise. #R##N#Our evaluation is two fold: First, we demonstrate the system performance on human annotated datasets. Second, for languages where no gold-standard benchmarks are available, we propose a new method, distant evaluation, based on statistical machine translation.

Connected through hyperedge HE-1 (induced by source paper "entity projection via machine translation for cross lingual ner"):
  with members:
    - "pioner datasets and baselines for armenian named entity recognition"
    - "adapting word embeddings to new languages with morphological and phonological subword representations"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"
    - "the iit bombay english hindi parallel corpus"
    - "word translation without parallel data"
    - "neural architectures for named entity recognition"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-2 (induced by source paper "fine grained entity recognition with reduced false negatives and large type coverage"):
  with members:
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "corpus level fine grained entity typing"
    - "label noise reduction in entity typing by heterogeneous partial label embedding"
    - "neural architectures for fine grained entity type classification"
    - "neural architectures for named entity recognition"
    - "named entity recognition with bidirectional lstm cnns"
    - "context dependent fine grained entity type tagging"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

Connected through hyperedge HE-3 (induced by source paper "design and implementation of an open source greek pos tagger and entity recognizer using spacy"):
  with members:
    - "learning word vectors for 157 languages"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-4 (induced by source paper "building a massive corpus for named entity recognition using free open data sources"):
  with members:
    - "boosting named entity recognition with neural character embeddings"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "robust lexical features for improved neural network named entity recognition"):
  with members:
    - "deep contextualized word representations"
    - "deep active learning for named entity recognition"
    - "neural reranking for named entity recognition"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "semi supervised sequence tagging with bidirectional language models"
    - "named entity recognition with stack residual lstm and trainable bias decoding"
    - "empower sequence labeling with task aware neural language model"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-6 (induced by source paper "distributed representation of multi sense words a loss driven approach"):
  with members:
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "one billion word benchmark for measuring progress in statistical language modeling"
    - "polyglot distributed word representations for multilingual nlp"
    - "efficient estimation of word representations in vector space"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-7 (induced by source paper "a characterwise windowed approach to hebrew morphological segmentation"):
  with members:
    - "universal word segmentation implementation and interpretation"
    - "neural word segmentation learning for chinese"
    - "tensorflow a system for large scale machine learning"
    - "a universal part of speech tagset"
    - "unsupervised discovery of morphemes"

Connected through hyperedge HE-8 (induced by source paper "weitergeleitet von journalistin the gendered presentation of professions on wikipedia"):
  with members:
    - "tie breaker using language models to quantify gender bias in sports journalism"
    - "women through the glass ceiling gender asymmetries in wikipedia"
    - "inferring gender from names on the web a comparative evaluation of gender detection methods"
    - "it s a man s wikipedia assessing gender inequality in an online encyclopedia"
    - "moodbar increasing new user retention in wikipedia through lightweight socialization"
    - "first women second sex gender bias in wikipedia"

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)