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: "margin based parallel corpus mining with multilingual sentence embeddings"
  Abstract: Machine translation is highly sensitive to the size and quality of the training data, which has led to an increasing interest in collecting and filtering large parallel corpora. In this paper, we propose a new method for this task based on multilingual sentence embeddings. In contrast to previous approaches, which rely on nearest neighbor retrieval with a hard threshold over cosine similarity, our proposed method accounts for the scale inconsistencies of this measure, considering the margin between a given sentence pair and its closest candidates instead. Our experiments show large improvements over existing methods. We outperform the best published results on the BUCC mining task and the UN reconstruction task by more than 10 F1 and 30 precision points, respectively. Filtering the English-German ParaCrawl corpus with our approach, we obtain 31.2 BLEU points on newstest2014, an improvement of more than one point over the best official filtered version.

Connected through hyperedge HE-1 (induced by source paper "contextual lensing of universal sentence representations"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "ccmatrix mining billions of high quality parallel sentences on the web"
    - "parameter efficient transfer learning for nlp"
    - "unsupervised cross lingual representation learning at scale"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "learning to few shot learn across diverse natural language classification tasks"

Connected through hyperedge HE-2 (induced by source paper "unsupervised question decomposition for question answering"):
  with members:
    - "break it down a question understanding benchmark"
    - "unsupervised question answering by cloze translation"
    - "compositional questions do not necessitate multi hop reasoning"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "self assembling modular networks for interpretable multi hop reasoning"
    - "revealing the importance of semantic retrieval for machine reading at scale"
    - "huggingface s transformers state of the art natural language processing"
    - "multi hop reading comprehension through question decomposition and rescoring"

Connected through hyperedge HE-3 (induced by source paper "mlqa evaluating cross lingual extractive question answering"):
  with members:
    - "neural arabic question answering"
    - "cross lingual transfer learning for question answering"
    - "beyond english only reading comprehension experiments in zero shot multilingual transfer for bulgarian"
    - "unsupervised question answering by cloze translation"
    - "synthetic qa corpora generation with roundtrip consistency"
    - "xcmrc evaluating cross lingual machine reading comprehension"
    - "korquad1 0 korean qa dataset for machine reading comprehension"
    - "xlda cross lingual data augmentation for natural language inference and question answering"

Connected through hyperedge HE-4 (induced by source paper "emu enhancing multilingual sentence embeddings with semantic specialization"):
  with members:
    - "sentence bert sentence embeddings using siamese bert networks"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "improving multilingual sentence embedding using bi directional dual encoder with additive margin softmax"
    - "multi lingual common semantic space construction via cluster consistent word embedding"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "unsupervised multilingual word embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "simple and effective paraphrastic similarity from parallel translations"):
  with members:
    - "parabank monolingual bitext generation and sentential paraphrasing via lexically constrained neural machine translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "filtering and mining parallel data in a joint multilingual space"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "deep contextualized word representations"
    - "extracting parallel sentences with bidirectional recurrent neural networks to improve machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "explicit cross lingual pre training for unsupervised machine translation"):
  with members:
    - "mass masked sequence to sequence pre training for language generation"
    - "unsupervised neural machine translation with smt as posterior regularization"
    - "cross lingual language model pretraining"
    - "unsupervised statistical machine translation"
    - "xnli evaluating cross lingual sentence representations"
    - "unsupervised neural machine translation initialized by unsupervised statistical machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint training for neural machine translation models with monolingual data"

Connected through hyperedge HE-7 (induced by source paper "learning bilingual sentence embeddings via autoencoding and computing similarities with a multilayer perceptron"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "the missing ingredient in zero shot neural machine translation"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "filtering and mining parallel data in a joint multilingual space"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "dual conditional cross entropy filtering of noisy parallel corpora"
    - "learning joint multilingual sentence representations with neural machine translation"
    - "an empirical analysis of nmt derived interlingual embeddings and their use in parallel sentence identification"

Connected through hyperedge HE-8 (induced by source paper "ccmatrix mining billions of high quality parallel sentences on the web"):
  with members:
    - "reducing transformer depth on demand with structured dropout"
    - "ccnet extracting high quality monolingual datasets from web crawl data"
    - "wikimatrix mining 135m parallel sentences in 1620 language pairs from wikipedia"
    - "achieving human parity on automatic chinese to english news translation"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "filtering and mining parallel data in a joint multilingual space"
    - "learning word vectors for 157 languages"
    - "an empirical analysis of nmt derived interlingual embeddings and their use in parallel sentence identification"

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)