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: "not all neural embeddings are born equal"
  Abstract: Neural language models learn word representations that capture rich linguistic and conceptual information. Here we investigate the embeddings learned by neural machine translation models. We show that translation-based embeddings outperform those learned by cutting-edge monolingual models at single-language tasks requiring knowledge of conceptual similarity and/or syntactic role. The findings suggest that, while monolingual models learn information about how concepts are related, neural-translation models better capture their true ontological status.

Connected through hyperedge HE-1 (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-2 (induced by source paper "direct network transfer transfer learning of sentence embeddings for semantic similarity"):
  with members:
    - "universal sentence encoder"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "an efficient framework for learning sentence representations"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "learning paraphrastic sentence embeddings from back translated bitext"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-3 (induced by source paper "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "learning to generate reviews and discovering sentiment"
    - "a continuously growing dataset of sentential paraphrases"
    - "understanding task design trade offs in crowdsourced paraphrase collection"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learned in translation contextualized word vectors"
    - "learning paraphrastic sentence embeddings from back translated bitext"

Connected through hyperedge HE-4 (induced by source paper "learning paraphrastic sentence embeddings from back translated bitext"):
  with members:
    - "revisiting recurrent networks for paraphrastic sentence embeddings"
    - "learning distributed representations of sentences from unlabelled data"
    - "charagram embedding words and sentences via character n grams"
    - "theano a python framework for fast computation of mathematical expressions"
    - "improving neural machine translation models with monolingual data"
    - "skip thought vectors"
    - "neural machine translation of rare words with subword units"
    - "towards universal paraphrastic sentence embeddings"

Connected through hyperedge HE-5 (induced by source paper "think globally embed locally locally linear meta embedding of words"):
  with members:
    - "on the convergent properties of word embedding methods"
    - "learning word meta embeddings by using ensembles of embedding sets"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "embedding word similarity with neural machine translation"
    - "the expressive power of word embeddings"
    - "distributed representations of words and phrases and their compositionality"
    - "from frequency to meaning vector space models of semantics"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

Connected through hyperedge HE-6 (induced by source paper "multichannel variable size convolution for sentence classification"):
  with members:
    - "deep learning for answer sentence selection"
    - "distributed representations of sentences and documents"
    - "modelling visualising and summarising documents with a single convolutional neural network"
    - "convolutional neural networks for sentence classification"
    - "a convolutional neural network for modelling sentences"
    - "speech recognition with deep recurrent neural networks"
    - "the expressive power of word embeddings"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "a dual embedding space model for document ranking"):
  with members:
    - "controlled experiments for word embeddings"
    - "distributed representations of sentences and documents"
    - "linking glove with word2vec"
    - "word2vec parameter learning explained"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "word representations via gaussian embedding"
    - "addressing the rare word problem in neural machine translation"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "new word analogy corpus for exploring embeddings of czech words"):
  with members:
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "polyglot distributed word representations for multilingual nlp"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

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)