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: "learning multilingual word representations using a bag of words autoencoder"
  Abstract: Recent work on learning multilingual word representations usually relies on the use of word-level alignements (e.g. infered with the help of GIZA++) between translated sentences, in order to align the word embeddings in different languages. In this workshop paper, we investigate an autoencoder model for learning multilingual word representations that does without such word-level alignements. The autoencoder is trained to reconstruct the bag-of-word representation of given sentence from an encoded representation extracted from its translation. We evaluate our approach on a multilingual document classification task, where labeled data is available only for one language (e.g. English) while classification must be performed in a different language (e.g. French). In our experiments, we observe that our method compares favorably with a previously proposed method that exploits word-level alignments to learn word representations.

Connected through hyperedge HE-1 (induced by source paper "modeling language variation and universals a survey on typological linguistics for natural language processing"):
  with members:
    - "morphological word embeddings"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "xnli evaluating cross lingual sentence representations"
    - "a deep generative model of vowel formant typology"
    - "deep contextualized word representations"
    - "deep probabilistic logic a unifying framework for indirect supervision"
    - "adversarial propagation and zero shot cross lingual transfer of word vector specialization"
    - "past present future a computational investigation of the typology of tense in 1000 languages"

Connected through hyperedge HE-2 (induced by source paper "person job fit adapting the right talent for the right job with joint representation learning"):
  with members:
    - "enhancing person job fit for talent recruitment an ability aware neural network approach"
    - "election with bribed voter uncertainty hardness and approximation algorithm"
    - "collaborative company profiling insights from an employee s perspective"
    - "measuring the popularity of job skills in recruitment market a multi criteria approach"
    - "comparative study of cnn and rnn for natural language processing"
    - "recruitment market trend analysis with sequential latent variable models"
    - "combining recurrent and convolutional neural networks for relation classification"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "feature extraction using latent dirichlet allocation and neural networks a case study on movie synopses"):
  with members:
    - "distributed representations of sentences and documents"
    - "probabilistic latent semantic analysis"
    - "distributional semantics beyond words supervised learning of analogy and paraphrase"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "probabilistic models for unified collaborative and content based recommendation in sparse data environments"
    - "expectation propogation for the generative aspect model"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-4 (induced by source paper "learning to represent words in context with multilingual supervision"):
  with members:
    - "trans gram fast cross lingual word embeddings"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "end to end attention based large vocabulary speech recognition"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "syntax aware multi sense word embeddings for deep compositional models of meaning"
    - "adam a method for stochastic optimization"
    - "embedding word similarity with neural machine translation"
    - "multilingual models for compositional distributed semantics"

Connected through hyperedge HE-5 (induced by source paper "separated by an un common language towards judgment language informed vector space modeling"):
  with members:
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "multilingual models for compositional distributed semantics"
    - "an autoencoder approach to learning bilingual word representations"
    - "polyglot distributed word representations for multilingual nlp"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "multilingual distributed representations without word alignment"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-6 (induced by source paper "distributed representations for compositional semantics"):
  with members:
    - "multilingual models for compositional distributed semantics"
    - "a convolutional neural network for modelling sentences"
    - "cutting recursive autoencoder trees"
    - "category theoretic quantitative compositional distributional models of natural language semantics"
    - "polyglot distributed word representations for multilingual nlp"
    - "efficient estimation of word representations in vector space"
    - "recurrent convolutional neural networks for discourse compositionality"
    - "multi step regression learning for compositional distributional semantics"

Connected through hyperedge HE-7 (induced by source paper "multilingual models for compositional distributed semantics"):
  with members:
    - "polyglot distributed word representations for multilingual nlp"
    - "efficient estimation of word representations in vector space"
    - "recurrent convolutional neural networks for discourse compositionality"
    - "exploiting similarities among languages for machine translation"
    - "multilingual distributed representations without word alignment"
    - "domain and function a dual space model of semantic relations and compositions"
    - "natural language processing almost from scratch"
    - "experimental support for a categorical compositional distributional model of meaning"

Connected through hyperedge HE-8 (induced by source paper "a deep architecture for semantic parsing"):
  with members:
    - "multilingual models for compositional distributed semantics"
    - "recurrent convolutional neural networks for discourse compositionality"
    - "multilingual distributed representations without word alignment"
    - "a joint model of language and perception for grounded attribute learning"
    - "natural language processing almost from scratch"
    - "learning dependency based compositional semantics"

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)