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: "pytext a seamless path from nlp research to production"
  Abstract: We introduce PyText - a deep learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of serving models at scale. It achieves this by providing simple and extensible interfaces for model components, and by using PyTorch's capabilities of exporting models for inference via the optimized Caffe2 execution engine. We report our own experience of migrating experimentation and production workflows to PyText, which enabled us to iterate faster on novel modeling ideas and then seamlessly ship them at industrial scale.

Connected through hyperedge HE-1 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations"):
  with members:
    - "deep bayesian network for visual question generation"
    - "incorporating visual semantics into sentence representations within a grounded space"
    - "visualizing and measuring the geometry of bert"
    - "lxmert learning cross modality encoder representations from transformers"
    - "bert rediscovers the classical nlp pipeline"
    - "context aware zero shot learning for object recognition"
    - "fusion of detected objects in text for visual question answering"
    - "supervised multimodal bitransformers for classifying images and text"

Connected through hyperedge HE-2 (induced by source paper "discriminative adversarial search for abstractive summarization"):
  with members:
    - "evaluating the factual consistency of abstractive text summarization"
    - "answers unite unsupervised metrics for reinforced summarization models"
    - "defending against neural fake news"
    - "bridging the gap between training and inference for neural machine translation"
    - "better rewards yield better summaries learning to summarise without references"
    - "analyzing uncertainty in neural machine translation"
    - "bottom up abstractive summarization"
    - "language gans falling short"

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 "bridging the domain gap in cross lingual document classification"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "a survey of cross lingual word embedding models"
    - "unsupervised data augmentation for consistency training"
    - "cross lingual language model pretraining"
    - "unsupervised domain adaptation of contextualized embeddings for sequence labeling"
    - "an embarrassingly simple approach for transfer learning from pretrained language models"
    - "a corpus for multilingual document classification in eight languages"
    - "xnli evaluating cross lingual sentence representations"

Connected through hyperedge HE-5 (induced by source paper "improving slot filling by utilizing contextual information"):
  with members:
    - "bert for joint intent classification and slot filling"
    - "gcdt a global context enhanced deep transition architecture for sequence labeling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mine mutual information neural estimation"
    - "rasa open source language understanding and dialogue management"
    - "sequential convolutional neural networks for slot filling in spoken language understanding"
    - "leveraging sentence level information with encoder lstm for natural language understanding"
    - "leveraging sentence level information with encoder lstm for semantic slot filling"

Connected through hyperedge HE-6 (induced by source paper "cross lingual transfer learning for multilingual task oriented dialog"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "polyglot contextual representations improve crosslingual transfer"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "on the strength of character language models for multilingual named entity recognition"
    - "parameter sharing between dependency parsers for related languages"
    - "zero shot cross lingual classification using multilingual neural machine translation"
    - "xnli evaluating cross lingual sentence representations"

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)