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: "learning paraphrastic sentence embeddings from back translated bitext"
  Abstract: We consider the problem of learning general-purpose, paraphrastic sentence embeddings in the setting of Wieting et al. (2016b). We use neural machine translation to generate sentential paraphrases via back-translation of bilingual sentence pairs. We evaluate the paraphrase pairs by their ability to serve as training data for learning paraphrastic sentence embeddings. We find that the data quality is stronger than prior work based on bitext and on par with manually-written English paraphrase pairs, with the advantage that our approach can scale up to generate large training sets for many languages and domains. We experiment with several language pairs and data sources, and develop a variety of data filtering techniques. In the process, we explore how neural machine translation output differs from human-written sentences, finding clear differences in length, the amount of repetition, and the use of rare words.

Connected through hyperedge HE-1 (induced by source paper "abstractive snippet generation"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "generating wikipedia by summarizing long sequences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "opennmt open source toolkit for neural machine translation"
    - "query based abstractive summarization using neural networks"
    - "diversity driven attention model for query based abstractive summarization"

Connected through hyperedge HE-2 (induced by source paper "multi head attention with diversity for learning grounded multilingual multimodal representations"):
  with members:
    - "lessons learned in multilingual grounded language learning"
    - "stacked cross attention for image text matching"
    - "learning visually grounded semantics from contrastive adversarial samples"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multi head attention with disagreement regularization"
    - "imagination improves multimodal translation"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "learning two branch neural networks for image text matching tasks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "still a pain in the neck evaluating text representations on lexical composition"):
  with members:
    - "semeval 2013 task 4 free paraphrases of noun compounds"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "linguistic knowledge and transferability of contextual representations"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "allennlp a deep semantic natural language processing platform"
    - "olive oil is made of olives baby oil is made for babies interpreting noun compounds using paraphrases in a neural model"
    - "deep contextualized word representations"
    - "word sense induction with neural bilm and symmetric patterns"

Connected through hyperedge HE-5 (induced by source paper "effective data augmentation approaches to end to end task oriented dialogue"):
  with members:
    - "adversarial over sensitivity and over stability strategies for dialogue models"
    - "talking to myself self dialogues as data for conversational agents"
    - "automatic data expansion for customer care spoken language understanding"
    - "sequence to sequence data augmentation for dialogue language understanding"
    - "key value retrieval networks for task oriented dialogue"
    - "latent intention dialogue models"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-6 (induced by source paper "a systematic comparison of english noun compound representations"):
  with members:
    - "still a pain in the neck evaluating text representations on lexical composition"
    - "allennlp a deep semantic natural language processing platform"
    - "olive oil is made of olives baby oil is made for babies interpreting noun compounds using paraphrases in a neural model"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "paraphrase to explicate revealing implicit noun compound relations"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-7 (induced by source paper "qanet combining local convolution with global self attention for reading comprehension"):
  with members:
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "learning to paraphrase for question answering"
    - "attention is all you need"
    - "stochastic answer networks for machine reading comprehension"
    - "learning recurrent span representations for extractive question answering"
    - "making neural qa as simple as possible but not simpler"
    - "globally normalized reader"
    - "reasonet learning to stop reading in machine comprehension"

Connected through hyperedge HE-8 (induced by source paper "an efficient framework for learning sentence representations"):
  with members:
    - "generating sentences by editing prototypes"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "discourse based objectives for fast unsupervised sentence representation learning"
    - "curiosity driven exploration by self supervised prediction"
    - "revisiting recurrent networks for paraphrastic sentence embeddings"
    - "learning generic sentence representations using convolutional neural networks"
    - "siamese cbow optimizing word embeddings for sentence representations"
    - "learning distributed representations of sentences from unlabelled 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)