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: "towards cross lingual distributed representations without parallel text trained with adversarial autoencoders"
  Abstract: Current approaches to learning vector representations of text that are compatible between different languages usually require some amount of parallel text, aligned at word, sentence or at least document level. We hypothesize however, that different natural languages share enough semantic structure that it should be possible, in principle, to learn compatible vector representations just by analyzing the monolingual distribution of words. #R##N#In order to evaluate this hypothesis, we propose a scheme to map word vectors trained on a source language to vectors semantically compatible with word vectors trained on a target language using an adversarial autoencoder. #R##N#We present preliminary qualitative results and discuss possible future developments of this technique, such as applications to cross-lingual sentence representations.

Connected through hyperedge HE-1 (induced by source paper "cross lingual alignment vs joint training a comparative study and a simple unified framework"):
  with members:
    - "adversarial learning with contextual embeddings for zero resource cross lingual classification and ner"
    - "a survey of cross lingual word embedding models"
    - "bilingual lexicon induction through unsupervised machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "density matching for bilingual word embedding"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "bilingual lexicon induction with semi supervision in non isometric embedding spaces"

Connected through hyperedge HE-2 (induced by source paper "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "learning cross lingual word embeddings from twitter via distant supervision"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "why is unsupervised alignment of english embeddings from different algorithms so hard"
    - "the limitations of cross language word embeddings evaluation"
    - "xnli evaluating cross lingual sentence representations"
    - "unsupervised multilingual word embeddings"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"

Connected through hyperedge HE-3 (induced by source paper "unsupervised clinical language translation"):
  with members:
    - "towards unsupervised speech to text translation"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "unsupervised statistical machine translation"
    - "mapping unparalleled clinical professional and consumer languages with embedding alignment"
    - "deep contextualized word representations"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "unsupervised multimodal representation learning across medical images and reports"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-4 (induced by source paper "meemi a simple method for post processing cross lingual word embeddings"):
  with members:
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "gromov wasserstein alignment of word embedding spaces"
    - "the limitations of cross language word embeddings evaluation"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"
    - "generalizing procrustes analysis for better bilingual dictionary induction"
    - "non adversarial unsupervised word translation"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-5 (induced by source paper "learning task specific representation for novel words in sequence labeling"):
  with members:
    - "chinese ner using lattice lstm"
    - "learning semantic representations for novel words leveraging both form and context"
    - "a la carte embedding cheap but effective induction of semantic feature vectors"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mimicking word embeddings using subword rnns"
    - "end to end learning for self driving cars"
    - "finding function in form compositional character models for open vocabulary word representation"

Connected through hyperedge HE-6 (induced by source paper "tracewalk semantic based process graph embedding for consistency checking"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "convolutional neural network architectures for matching natural language sentences"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "learning multilingual word embeddings in latent metric space a geometric approach"):
  with members:
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "universal neural machine translation for extremely low resource languages"
    - "generalizing procrustes analysis for better bilingual dictionary induction"
    - "non adversarial unsupervised word translation"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"

Connected through hyperedge HE-8 (induced by source paper "towards unsupervised speech to text translation"):
  with members:
    - "improving unsupervised word by word translation with language model and denoising autoencoder"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "unsupervised statistical machine translation"
    - "end to end automatic speech translation of audiobooks"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

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)