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: "robust multilingual part of speech tagging via adversarial training"
  Abstract: Adversarial training (AT) is a powerful regularization method for neural networks, aiming to achieve robustness to input perturbations. Yet, the specific effects of the robustness obtained by AT are still unclear in the context of natural language processing. In this paper, we propose and analyze a neural POS tagging model that exploits adversarial training (AT). In our experiments on the Penn Treebank WSJ corpus and the Universal Dependencies (UD) dataset (28 languages), we find that AT not only improves the overall tagging accuracy, but also 1) largely prevents overfitting in low resource languages and 2) boosts tagging accuracy for rare / unseen words. The proposed POS tagger achieves state-of-the-art performance on nearly all of the languages in UD v1.2. We also demonstrate that 3) the improved tagging performance by AT contributes to the downstream task of dependency parsing, and that 4) AT helps the model to learn cleaner word and internal representations. These positive results motivate further use of AT for natural language tasks.

Connected through hyperedge HE-1 (induced by source paper "linguistic knowledge and transferability of contextual representations"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "what can linguistics and deep learning contribute to each other"
    - "what do rnn language models learn about filler gap dependencies"
    - "what s going on in neural constituency parsers an analysis"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "why self attention a targeted evaluation of neural machine translation architectures"

Connected through hyperedge HE-2 (induced by source paper "hierarchically refined label attention network for sequence labeling"):
  with members:
    - "benchmarking approximate inference methods for neural structured prediction"
    - "design challenges and misconceptions in neural sequence labeling"
    - "two local models for neural constituent parsing"
    - "joint embedding of words and labels for text classification"
    - "semi supervised sequence modeling with cross view training"
    - "cross target stance classification with self attention networks"
    - "ncrf an open source neural sequence labeling toolkit"
    - "learning approximate inference networks for structured prediction"

Connected through hyperedge HE-3 (induced by source paper "improving machine reading comprehension via adversarial training"):
  with members:
    - "option comparison network for multiple choice reading comprehension"
    - "spanbert improving pre training by representing and predicting spans"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "robust machine comprehension models via adversarial training"
    - "exploring machine reading comprehension with explicit knowledge"
    - "know what you don t know unanswerable questions for squad"
    - "coqa a conversational question answering challenge"

Connected through hyperedge HE-4 (induced by source paper "cross lingual dependency parsing with unlabeled auxiliary languages"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "cross lingual syntactic transfer through unsupervised adaptation of invertible projections"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "low resource syntactic transfer with unsupervised source reordering"
    - "multinomial adversarial networks for multi domain text classification"
    - "adversarial neural networks for cross lingual sequence tagging"
    - "self attention with relative position representations"

Connected through hyperedge HE-5 (induced by source paper "sequence tagging with contextual and non contextual subword representations a multilingual evaluation"):
  with members:
    - "efficient contextual representation learning without softmax layer"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"
    - "bpemb tokenization free pre trained subword embeddings in 275 languages"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"

Connected through hyperedge HE-6 (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"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "contextualized non local neural networks for sequence learning"):
  with members:
    - "deep contextualized word representations"
    - "relational inductive biases deep learning and graph networks"
    - "constituency parsing with a self attentive encoder"
    - "non local neural networks"
    - "attention is all you need"
    - "neural message passing for quantum chemistry"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-8 (induced by source paper "data augmentation for neural online chat response selection"):
  with members:
    - "sharp nearby fuzzy far away how neural language models use context"
    - "data augmentation for low resource neural machine translation"
    - "learning to paraphrase for question answering"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "data noising as smoothing in neural network language models"
    - "semi supervised qa with generative domain adaptive nets"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"

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)