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: "how transferable are neural networks in nlp applications"
  Abstract: Transfer learning is aimed to make use of valuable knowledge in a source domain to help model performance in a target domain. It is particularly important to neural networks, which are very likely to be overfitting. In some fields like image processing, many studies have shown the effectiveness of neural network-based transfer learning. For neural NLP, however, existing studies have only casually applied transfer learning, and conclusions are inconsistent. In this paper, we conduct systematic case studies and provide an illuminating picture on the transferability of neural networks in NLP.

Connected through hyperedge HE-1 (induced by source paper "temporal embeddings and transformer models for narrative text understanding"):
  with members:
    - "extraction and analysis of fictional character networks a survey"
    - "contextualized word embeddings enhanced event temporal relation extraction for story understanding"
    - "training temporal word embeddings with a compass"
    - "story ending prediction by transferable bert"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "diachronic word embeddings and semantic shifts a survey"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "on architectures for including visual information in neural language models for image description"):
  with members:
    - "transfer learning from language models to image caption generators better models may not transfer better"
    - "visual entailment a novel task for fine grained image understanding"
    - "do better imagenet models transfer better"
    - "quantifying the amount of visual information used by neural caption generators"
    - "pre gen metrics predicting caption quality metrics without generating captions"
    - "object counts bringing explicit detections back into image captioning"
    - "the memad submission to the wmt18 multimodal translation task"
    - "grounded textual entailment"

Connected through hyperedge HE-3 (induced by source paper "to tune or not to tune adapting pretrained representations to diverse tasks"):
  with members:
    - "rethinking imagenet pre training"
    - "do better imagenet models transfer better"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "scalable mutual information estimation using dependence graphs"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an efficient framework for learning sentence representations"

Connected through hyperedge HE-4 (induced by source paper "on the difficulty of warm starting neural network training"):
  with members:
    - "generalization in deep networks the role of distance from initialization"
    - "an analysis of pre training on object detection"
    - "deep batch active learning by diverse uncertain gradient lower bounds"
    - "energy and policy considerations for deep learning in nlp"
    - "green ai"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "model based active exploration"

Connected through hyperedge HE-5 (induced by source paper "learning to selectively transfer reinforced transfer learning for deep text matching"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "reinforced co training"
    - "end task oriented textual entailment via deep explorations of inter sentence interactions"
    - "learning sampling policies for domain adaptation"
    - "reinforcement learning for relation classification from noisy data"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"
    - "response ranking with deep matching networks and external knowledge in information seeking conversation systems"
    - "learning how to active learn a deep reinforcement learning approach"

Connected through hyperedge HE-6 (induced by source paper "discovery of natural language concepts in individual units of cnns"):
  with members:
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "constituency parsing with a self attentive encoder"
    - "learning to generate reviews and discovering sentiment"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "network dissection quantifying interpretability of deep visual representations"
    - "attention is all you need"
    - "interpreting deep visual representations via network dissection"
    - "what do neural machine translation models learn about morphology"

Connected through hyperedge HE-7 (induced by source paper "unsupervised domain adaptation of contextualized embeddings for sequence labeling"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "learning and evaluating general linguistic intelligence"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "using similarity measures to select pretraining data for ner"
    - "unsupervised data augmentation for consistency training"
    - "evaluating historical text normalization systems how well do they generalize"

Connected through hyperedge HE-8 (induced by source paper "transfer learning from language models to image caption generators better models may not transfer better"):
  with members:
    - "universal language model fine tuning for text classification"
    - "where to put the image in an image caption generator"
    - "spice semantic propositional image caption evaluation"
    - "re evaluating automatic metrics for image captioning"
    - "unsupervised pretraining for sequence to sequence learning"
    - "transfer learning for low resource neural machine translation"
    - "language models for image captioning the quirks and what works"
    - "learning like a child fast novel visual concept learning from sentence descriptions of images"

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)