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: "strong baselines for neural semi supervised learning under domain shift"
  Abstract: Novel neural models have been proposed in recent years for learning under domain shift. Most models, however, only evaluate on a single task, on proprietary datasets, or compare to weak baselines, which makes comparison of models difficult. In this paper, we re-evaluate classic general-purpose bootstrapping approaches in the context of neural networks under domain shifts vs. recent neural approaches and propose a novel multi-task tri-training method that reduces the time and space complexity of classic tri-training. Extensive experiments on two benchmarks are negative: while our novel method establishes a new state-of-the-art for sentiment analysis, it does not fare consistently the best. More importantly, we arrive at the somewhat surprising conclusion that classic tri-training, with some additions, outperforms the state of the art. We conclude that classic approaches constitute an important and strong baseline.

Connected through hyperedge HE-1 (induced by source paper "pseudo labeling and negative feedback learning for large scale multi label domain classification"):
  with members:
    - "learning from dialogue after deployment feed yourself chatbot"
    - "a scalable neural shortlisting reranking approach for large scale domain classification in natural language understanding"
    - "efficient large scale domain classification with personalized attention"
    - "classification from positive unlabeled and biased negative data"
    - "supervised domain enablement attention for personalized domain classification"
    - "just ask building an architecture for extensible self service spoken language understanding"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-2 (induced by source paper "diversity based generalization for neural unsupervised text classification under domain shift"):
  with members:
    - "transfer learning for sequences via learning to collocate"
    - "domain adaptation with adversarial training and graph embeddings"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "distributed representations of words and phrases and their compositionality"
    - "marginalized denoising autoencoders for domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "regularizing proxies with multi adversarial training for unsupervised domain adaptive semantic segmentation"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "adaptive semantic segmentation with a strategic curriculum of proxy labels"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "adversarial learning for semi supervised semantic segmentation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "deep contextualized self training for low resource dependency parsing"):
  with members:
    - "viable dependency parsing as sequence labeling"
    - "a survey on recent advances in named entity recognition from deep learning models"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "no training required exploring random encoders for sentence classification"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "distant supervision from disparate sources for low resource part of speech tagging"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "text processing like humans do visually attacking and shielding nlp systems"):
  with members:
    - "is it time to swish comparing deep learning activation functions across nlp tasks"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "generating natural language adversarial examples"
    - "deep contextualized word representations"
    - "shielding google s language toxicity model against adversarial attacks"
    - "cross lingual argumentation mining machine translation and a bit of projection is all you need"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "attacking visual language grounding with adversarial examples a case study on neural image captioning"

Connected through hyperedge HE-6 (induced by source paper "not enough data deep learning to the rescue"):
  with members:
    - "data augmentation using gans"
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "improving short text classification through global augmentation methods"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "deep contextualized word representations"
    - "conditional bert contextual augmentation"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "leveraging just a few keywords for fine grained aspect detection through weakly supervised co training"):
  with members:
    - "everything old is new again a multi view learning approach to learning using privileged information and distillation"
    - "deep learning for sentiment analysis a survey"
    - "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"
    - "learning from noisy singly labeled data"

Connected through hyperedge HE-8 (induced by source paper "improving robustness of task oriented dialog systems"):
  with members:
    - "unsupervised data augmentation for consistency training"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "efficient augmentation via data subsampling"
    - "onenet joint domain intent slot prediction for spoken language understanding"
    - "generating natural language adversarial examples"
    - "constructing unrestricted adversarial examples with generative models"
    - "cross lingual transfer learning for multilingual task oriented dialog"
    - "certified defenses against adversarial examples"

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)