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: "trans gram fast cross lingual word embeddings"
  Abstract: We introduce Trans-gram, a simple and computationally-efficient method to simultaneously learn and align wordembeddings for a variety of languages, using only monolingual data and a smaller set of sentence-aligned data. We use our new method to compute aligned wordembeddings for twenty-one languages using English as a pivot language. We show that some linguistic features are aligned across languages for which we do not have aligned data, even though those properties do not exist in the pivot language. We also achieve state of the art results on standard cross-lingual text classification and word translation tasks.

Connected through hyperedge HE-1 (induced by source paper "robust cross lingual embeddings from parallel sentences"):
  with members:
    - "crosslingual document embedding as reduced rank ridge regression"
    - "a survey of cross lingual word embedding models"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "better word embeddings by disentangling contextual n gram information"
    - "a corpus for multilingual document classification in eight languages"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "unsupervised multilingual word embeddings"

Connected through hyperedge HE-2 (induced by source paper "towards learning cross modal perception trace models"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning two branch neural networks for image text matching tasks"
    - "attention is all you need"
    - "beyond bilingual multi sense word embeddings using multilingual context"
    - "enriching word vectors with subword information"
    - "yfcc100m the new data in multimedia research"

Connected through hyperedge HE-3 (induced by source paper "the missing ingredient in zero shot neural machine translation"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "universal neural machine translation for extremely low resource languages"
    - "triangular architecture for rare language translation"
    - "a neural interlingua for multilingual machine translation"
    - "contextual parameter generation for universal neural machine translation"
    - "phrase based neural unsupervised machine translation"
    - "zero shot dual machine translation"
    - "unsupervised neural machine translation with weight sharing"

Connected through hyperedge HE-4 (induced by source paper "graph based bilingual word embedding for statistical machine translation"):
  with members:
    - "syntax directed attention for neural machine translation"
    - "mimicking word embeddings using subword rnns"
    - "chunk based bi scale decoder for neural machine translation"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "a simple regularization based algorithm for learning cross domain word embeddings"
    - "six challenges for neural machine translation"
    - "bilingual learning of multi sense embeddings with discrete autoencoders"
    - "cross lingual models of word embeddings an empirical comparison"

Connected through hyperedge HE-5 (induced by source paper "learning joint multilingual sentence representations with neural machine translation"):
  with members:
    - "supervised learning of universal sentence representations from natural language inference data"
    - "billion scale similarity search with gpus"
    - "a correlational encoder decoder architecture for pivot based sequence generation"
    - "deep recurrent models with fast forward connections for neural machine translation"
    - "learning natural language inference using bidirectional lstm model and inner attention"
    - "multi source neural translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "google s multilingual neural machine translation system enabling zero shot translation"

Connected through hyperedge HE-6 (induced by source paper "clustering comparable corpora of russian and ukrainian academic texts word embeddings and semantic fingerprints"):
  with members:
    - "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 "learning to represent words in context with multilingual supervision"):
  with members:
    - "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"
    - "learning multilingual word representations using a bag of words autoencoder"
    - "embedding word similarity with neural machine translation"
    - "multilingual models for compositional distributed 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)