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: "unicoder a universal language encoder by pre training with multiple cross lingual tasks"
  Abstract: We present Unicoder, a universal language encoder that is insensitive to different languages. Given an arbitrary NLP task, a model can be trained with Unicoder using training data in one language and directly applied to inputs of the same task in other languages. Comparing to similar efforts such as Multilingual BERT and XLM, three new cross-lingual pre-training tasks are proposed, including cross-lingual word recovery, cross-lingual paraphrase classification and cross-lingual masked language model. These tasks help Unicoder learn the mappings among different languages from more perspectives. We also find that doing fine-tuning on multiple languages together can bring further improvement. Experiments are performed on two tasks: cross-lingual natural language inference (XNLI) and cross-lingual question answering (XQA), where XLM is our baseline. On XNLI, 1.8% averaged accuracy improvement (on 15 languages) is obtained. On XQA, which is a new cross-lingual dataset built by us, 5.5% averaged accuracy improvement (on French and German) is obtained.

Connected through hyperedge HE-1 (induced by source paper "xgpt cross modal generative pre training for image captioning"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "fusion of detected objects in text for visual question answering"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "attention on attention for image captioning"
    - "mass masked sequence to sequence pre training for language generation"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "videobert a joint model for video and language representation learning"

Connected through hyperedge HE-2 (induced by source paper "unicoder vl a universal encoder for vision and language by cross modal pre training"):
  with members:
    - "uniter learning universal image text representations"
    - "fusion of detected objects in text for visual question answering"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "videobert a joint model for video and language representation learning"
    - "visualbert a simple and performant baseline for vision and language"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "position focused attention network for image text matching"

Connected through hyperedge HE-3 (induced by source paper "emerging cross lingual structure in pretrained language models"):
  with members:
    - "similarity of neural network representations revisited"
    - "cross lingual bert transformation for zero shot dependency parsing"
    - "polyglot contextual representations improve crosslingual transfer"
    - "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"
    - "investigating multilingual nmt representations at scale"
    - "wikimatrix mining 135m parallel sentences in 1620 language pairs from wikipedia"

Connected through hyperedge HE-4 (induced by source paper "unsupervised cross lingual representation learning at scale"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "evaluating the cross lingual effectiveness of massively multilingual neural machine translation"
    - "massively multilingual neural machine translation in the wild findings and challenges"
    - "multilingual neural machine translation with knowledge distillation"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "unsupervised data augmentation for consistency training"
    - "ccnet extracting high quality monolingual datasets from web crawl data"
    - "mlqa evaluating cross lingual extractive question answering"

Connected through hyperedge HE-5 (induced by source paper "on the cross lingual transferability of monolingual representations"):
  with members:
    - "multilingual alignment of contextual word representations"
    - "parameter efficient transfer learning for nlp"
    - "paws paraphrase adversaries from word scrambling"
    - "cross lingual ability of multilingual bert an empirical study"
    - "bilingual lexicon induction through unsupervised machine translation"
    - "wic the word in context dataset for evaluating context sensitive meaning representations"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "paws x a cross lingual adversarial dataset for paraphrase identification"

Connected through hyperedge HE-6 (induced by source paper "cross lingual pre training based transfer for zero shot neural machine translation"):
  with members:
    - "pivot based transfer learning for neural machine translation between non english languages"
    - "improved zero shot neural machine translation via ignoring spurious correlations"
    - "consistency by agreement in zero shot neural machine translation"
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "unsupervised neural machine translation with smt as posterior regularization"
    - "how multilingual is multilingual bert"
    - "cross lingual language model pretraining"
    - "massively multilingual neural machine translation"

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)