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: "portuguese word embeddings evaluating on word analogies and natural language tasks"
  Abstract: Word embeddings have been found to provide meaningful representations for words in an efficient way; therefore, they have become common in Natural Language Processing sys- tems. In this paper, we evaluated different word embedding models trained on a large Portuguese corpus, including both Brazilian and European variants. We trained 31 word embedding models using FastText, GloVe, Wang2Vec and Word2Vec. We evaluated them intrinsically on syntactic and semantic analogies and extrinsically on POS tagging and sentence semantic similarity tasks. The obtained results suggest that word analogies are not appropriate for word embedding evaluation; task-specific evaluations appear to be a better option.

Connected through hyperedge HE-1 (induced by source paper "facebook ads monitor an independent auditing system for political ads on facebook"):
  with members:
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "inside the right leaning echo chambers characterizing gab an unmoderated social system"
    - "when politicians talk about politics identifying political tweets of brazilian congressmen"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "participatory militias an analysis of an armed movement s online audience"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "brazilian lyrics based music genre classification using a blstm network"):
  with members:
    - "lyrics based music genre classification using a hierarchical attention network"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "proactive security embedded ai solution for violent and abusive speech recognition"):
  with members:
    - "acoustic modeling using a shallow cnn htsvm architecture"
    - "do multi sense embeddings improve natural language understanding"
    - "recursive neural networks can learn logical semantics"
    - "efficient estimation of word representations in vector space"
    - "natural language processing almost from scratch"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-4 (induced by source paper "learning word vectors for 157 languages"):
  with members:
    - "advances in pre training distributed word representations"
    - "enriching word vectors with subword information"
    - "new word analogy corpus for exploring embeddings of czech words"
    - "polyglot distributed word representations for multilingual nlp"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "is there gender bias and stereotype in portuguese word embeddings"):
  with members:
    - "themis ml a fairness aware machine learning interface for end to end discrimination discovery and mitigation"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "a survey on measuring indirect discrimination in machine learning"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "when politicians talk about politics identifying political tweets of brazilian congressmen"):
  with members:
    - "recognizing explicit and implicit hate speech using a weakly supervised two path bootstrapping approach"
    - "how many political parties should brazil have a data driven method to assess and reduce fragmentation in multi party political systems"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "multi label classification of user reactions in online news"):
  with members:
    - "distant supervision for emotion detection using facebook reactions"

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)