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: "leveraging monolingual data for crosslingual compositional word representations"
  Abstract: In this work, we present a novel neural network based architecture for inducing compositional crosslingual word representations. Unlike previously proposed methods, our method fulfills the following three criteria; it constrains the word-level representations to be compositional, it is capable of leveraging both bilingual and monolingual data, and it is scalable to large vocabularies and large quantities of data. The key component of our approach is what we refer to as a monolingual inclusion criterion, that exploits the observation that phrases are more closely semantically related to their sub-phrases than to other randomly sampled phrases. We evaluate our method on a well-established crosslingual document classification task and achieve results that are either comparable, or greatly improve upon previous state-of-the-art methods. Concretely, our method reaches a level of 92.7% and 84.4% accuracy for the English to German and German to English sub-tasks respectively. The former advances the state of the art by 0.9% points of accuracy, the latter is an absolute improvement upon the previous state of the art by 7.7% points of accuracy and an improvement of 33.0% in error reduction.

Connected through hyperedge HE-1 (induced by source paper "scalable cross lingual transfer of neural sentence embeddings"):
  with members:
    - "context aware cross lingual mapping"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "a corpus for multilingual document classification in eight languages"
    - "evaluation of unsupervised compositional representations"
    - "evaluating compositionality in sentence embeddings"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "learning joint multilingual sentence representations with neural machine translation"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-2 (induced by source paper "a stronger baseline for multilingual word embeddings"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "unsupervised multilingual word embeddings"
    - "embedding learning through multilingual concept induction"
    - "word translation without parallel data"
    - "cross lingual models of word embeddings an empirical comparison"
    - "a strong baseline for learning cross lingual word embeddings from sentence alignments"
    - "massively multilingual word embeddings"
    - "multilingual models for compositional distributed semantics"

Connected through hyperedge HE-3 (induced by source paper "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"):
  with members:
    - "cross lingual models of word embeddings an empirical comparison"
    - "a network based end to end trainable task oriented dialogue system"
    - "a dual embedding space model for document ranking"
    - "simverb 3500 a large scale evaluation set of verb similarity"
    - "learning crosslingual word embeddings without bilingual corpora"
    - "charagram embedding words and sentences via character n grams"
    - "neural belief tracker data driven dialogue state tracking"
    - "many languages one parser"

Connected through hyperedge HE-4 (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"
    - "embedding word similarity with neural machine translation"
    - "multilingual models for compositional distributed semantics"
    - "conditional random field autoencoders for unsupervised structured prediction"

Connected through hyperedge HE-5 (induced by source paper "bilingual distributed word representations from document aligned comparable data"):
  with members:
    - "skip thought vectors"
    - "learning to understand phrases by embedding the dictionary"
    - "modeling order in neural word embeddings at scale"
    - "distributed representations of sentences and documents"
    - "improving zero shot learning by mitigating the hubness problem"
    - "multilingual models for compositional distributed semantics"
    - "evaluating neural word representations in tensor based compositional settings"
    - "an autoencoder approach to learning bilingual word representations"

Connected through hyperedge HE-6 (induced by source paper "bridge correlational neural networks for multilingual multimodal representation learning"):
  with members:
    - "a deep and autoregressive approach for topic modeling of multimodal data"
    - "correlational neural networks"
    - "multilingual models for compositional distributed semantics"
    - "caffe convolutional architecture for fast feature embedding"
    - "an autoencoder approach to learning bilingual word representations"
    - "exploiting similarities among languages for machine translation"
    - "multilingual distributed representations without word alignment"
    - "improving statistical machine translation for a resource poor language using related resource rich languages"

Connected through hyperedge HE-7 (induced by source paper "correlational neural networks"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "multilingual models for compositional distributed semantics"
    - "an autoencoder approach to learning bilingual word representations"
    - "exploiting similarities among languages for machine translation"
    - "multilingual distributed representations without word alignment"
    - "natural language processing almost from scratch"
    - "a kernel method for canonical correlation analysis"

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)