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: "cross lingual bert transformation for zero shot dependency parsing"
  Abstract: This paper investigates the problem of learning cross-lingual representations in a contextual space. We propose Cross-Lingual BERT Transformation (CLBT), a simple and efficient approach to generate cross-lingual contextualized word embeddings based on publicly available pre-trained BERT models (Devlin et al., 2018). In this approach, a linear transformation is learned from contextual word alignments to align the contextualized embeddings independently trained in different languages. We demonstrate the effectiveness of this approach on zero-shot cross-lingual transfer parsing. Experiments show that our embeddings substantially outperform the previous state-of-the-art that uses static embeddings. We further compare our approach with XLM (Lample and Conneau, 2019), a recently proposed cross-lingual language model trained with massive parallel data, and achieve highly competitive results.

Connected through hyperedge HE-1 (induced by source paper "multilingual alignment of contextual word representations"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "how multilingual is multilingual bert"
    - "context aware cross lingual mapping"
    - "simple and effective paraphrastic similarity from parallel translations"
    - "cross lingual language model pretraining"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "xnli evaluating cross lingual sentence representations"
    - "unsupervised multilingual word embeddings"

Connected through hyperedge HE-2 (induced by source paper "emerging cross lingual structure in pretrained language models"):
  with members:
    - "similarity of neural network representations revisited"
    - "unicoder a universal language encoder by pre training with multiple cross lingual tasks"
    - "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-3 (induced by source paper "emergent properties of finetuned language representation models"):
  with members:
    - "q8bert quantized 8bit bert"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "q bert hessian based ultra low precision quantization of bert"
    - "ctrl a conditional transformer language model for controllable generation"
    - "revealing the dark secrets of bert"
    - "visualizing and understanding the effectiveness of bert"
    - "how to fine tune bert for text classification"

Connected through hyperedge HE-4 (induced by source paper "how language neutral is multilingual bert"):
  with members:
    - "is multilingual bert fluent in language generation"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarial removal of demographic attributes from text data"

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)