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: "embedding word similarity with neural machine translation"
  Abstract: Neural language models learn word representations, or embeddings, that capture rich linguistic and conceptual information. Here we investigate the embeddings learned by neural machine translation models, a recently-developed class of neural language model. We show that embeddings from translation models outperform those learned by monolingual models at tasks that require knowledge of both conceptual similarity and lexical-syntactic role. We further show that these effects hold when translating from both English to French and English to German, and argue that the desirable properties of translation embeddings should emerge largely independently of the source and target languages. Finally, we apply a new method for training neural translation models with very large vocabularies, and show that this vocabulary expansion algorithm results in minimal degradation of embedding quality. Our embedding spaces can be queried in an online demo and downloaded from our web page. Overall, our analyses indicate that translation-based embeddings should be used in applications that require concepts to be organised according to similarity and/or lexical function, while monolingual embeddings are better suited to modelling (nonspecific) inter-word relatedness.

Connected through hyperedge HE-1 (induced by source paper "deep generative model for joint alignment and word representation"):
  with members:
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learning to embed words in context for syntactic tasks"
    - "cross lingual models of word embeddings an empirical comparison"
    - "generating sentences from a continuous space"
    - "automatic differentiation in machine learning a survey"
    - "a large annotated corpus for learning natural language inference"
    - "adam a method for stochastic optimization"
    - "multilingual models for compositional distributed semantics"

Connected through hyperedge HE-2 (induced by source paper "using deep neural networks to translate multi lingual threat intelligence"):
  with members:
    - "thinking fast and slow combining vector spaces and knowledge graphs"
    - "bridging neural machine translation and bilingual dictionaries"
    - "neural machine translation of rare words with subword units"
    - "neural machine translation by jointly learning to align and translate"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

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"
    - "not all neural embeddings are born equal"
    - "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 "word and phrase translation with word2vec"):
  with members:
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-7 (induced by source paper "charagram embedding words and sentences via character n grams"):
  with members:
    - "character based neural machine translation"
    - "counter fitting word vectors to linguistic constraints"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "learning distributed representations of sentences from unlabelled data"
    - "exploring the limits of language modeling"
    - "theano a python framework for fast computation of mathematical expressions"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "character based neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "bilingual learning of multi sense embeddings with discrete autoencoders"):
  with members:
    - "learning to represent words in context with multilingual supervision"
    - "breaking sticks and ambiguities with adaptive skip gram"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "do multi sense embeddings improve natural language understanding"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "leveraging monolingual data for crosslingual compositional word representations"
    - "multilingual models for compositional distributed semantics"
    - "conditional random field autoencoders for unsupervised structured prediction"

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)