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: "ultradense word embeddings by orthogonal transformation"
  Abstract: Embeddings are generic representations that are useful for many NLP tasks. In this paper, we introduce DENSIFIER, a method that learns an orthogonal transformation of the embedding space that focuses the information relevant for a task in an ultradense subspace of a dimensionality that is smaller by a factor of 100 than the original space. We show that ultradense embeddings generated by DENSIFIER reach state of the art on a lexicon creation task in which words are annotated with three types of lexical information - sentiment, concreteness and frequency. On the SemEval2015 10B sentiment analysis task we show that no information is lost when the ultradense subspace is used, but training is an order of magnitude more efficient due to the compactness of the ultradense space.

Connected through hyperedge HE-1 (induced by source paper "improving pre trained multilingual models with vocabulary expansion"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "gromov wasserstein alignment of word embedding spaces"
    - "generating wikipedia by summarizing long sequences"
    - "bilingual expert can find translation errors"
    - "universal neural machine translation for extremely low resource languages"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "on the limitations of unsupervised bilingual dictionary induction"

Connected through hyperedge HE-2 (induced by source paper "cate category name guidedword embedding"):
  with members:
    - "guiding corpus based set expansion by auxiliary sets generation and co expansion"
    - "spherical text embedding"
    - "topic modeling in embedding spaces"
    - "higitclass keyword driven hierarchical classification of github repositories"
    - "improving topic models with latent feature word representations"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "poincar e glove hyperbolic word embeddings"
    - "distilled wasserstein learning for word embedding and topic modeling"

Connected through hyperedge HE-3 (induced by source paper "analyzing structures in the semantic vector space a framework for decomposing word embeddings"):
  with members:
    - "imparting interpretability to word embeddings while preserving semantic structure"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "concatenated p mean word embeddings as universal cross lingual sentence representations"
    - "rotations and interpretability of word embeddings the case of the russian language"
    - "recent trends in deep learning based natural language processing"
    - "intrinsic subspace evaluation of word embedding representations"
    - "linear algebraic structure of word senses with applications to polysemy"

Connected through hyperedge HE-4 (induced by source paper "retrofitting contextualized word embeddings with paraphrases"):
  with members:
    - "on the convergence of adam and beyond"
    - "energy and policy considerations for deep learning in nlp"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "learned in translation contextualized word vectors"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"

Connected through hyperedge HE-5 (induced by source paper "subjective databases"):
  with members:
    - "voyageur an experiential travel search engine"
    - "deep learning for sentiment analysis a survey"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learning a neural semantic parser from user feedback"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"

Connected through hyperedge HE-6 (induced by source paper "attentive mimicking better word embeddings by attending to informative contexts"):
  with members:
    - "incorporating subword information into matrix factorization word embeddings"
    - "evaluating word embeddings in multi label classification using fine grained name typing"
    - "learning semantic representations for novel words leveraging both form and context"
    - "attention is all you need"
    - "mimicking word embeddings using subword rnns"
    - "high risk learning acquiring new word vectors from tiny data"
    - "morph fitting fine tuning word vector spaces with simple language specific rules"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-7 (induced by source paper "rotate textit king to get textit queen word relationships as orthogonal transformations in embedding space"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "gender bias in contextualized word embeddings"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "what does bert look at an analysis of bert s attention"
    - "understanding undesirable word embedding associations"
    - "beyond word importance contextual decomposition to extract interactions from lstms"
    - "attention is all you need"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-8 (induced by source paper "rotate king to get queen word relationships as orthogonal transformations in embedding space"):
  with members:
    - "gender bias in contextualized word embeddings"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "understanding undesirable word embedding associations"
    - "towards understanding linear word analogies"
    - "beyond word importance contextual decomposition to extract interactions from lstms"
    - "attention is all you need"
    - "enriching word vectors with subword information"
    - "man is to computer programmer as woman is to homemaker debiasing 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)