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: "paws paraphrase adversaries from word scrambling"
  Abstract: Existing paraphrase identification datasets lack sentence pairs that have high lexical overlap without being paraphrases. Models trained on such data fail to distinguish pairs like flights from New York to Florida and flights from Florida to New York. This paper introduces PAWS (Paraphrase Adversaries from Word Scrambling), a new dataset with 108,463 well-formed paraphrase and non-paraphrase pairs with high lexical overlap. Challenging pairs are generated by controlled word swapping and back translation, followed by fluency and paraphrase judgments by human raters. State-of-the-art models trained on existing datasets have dismal performance on PAWS (<40% accuracy); however, including PAWS training data for these models improves their accuracy to 85% while maintaining performance on existing tasks. In contrast, models that do not capture non-local contextual information fail even with PAWS training examples. As such, PAWS provides an effective instrument for driving further progress on models that better exploit structure, context, and pairwise comparisons.

Connected through hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity"):
  with members:
    - "multilingual alignment of contextual word representations"
    - "emerging cross lingual structure in pretrained language models"
    - "an empirical study on post processing methods for word embeddings"
    - "informing unsupervised pretraining with external linguistic knowledge"
    - "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"
    - "word similarity datasets for thai construction and evaluation"
    - "cross lingual ability of multilingual bert an empirical study"
    - "on the importance of subword information for morphological tasks in truly low resource languages"

Connected through hyperedge HE-2 (induced by source paper "superglue a stickier benchmark for general purpose language understanding systems"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "a surprisingly robust trick for the winograd schema challenge"
    - "multi task deep neural networks for natural language understanding"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "human vs muppet a conservative estimate of human performance on the glue benchmark"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"

Connected through hyperedge HE-3 (induced by source paper "flaubert unsupervised language model pre training for french"):
  with members:
    - "arabert transformer based model for arabic language understanding"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "reducing transformer depth on demand with structured dropout"
    - "very deep self attention networks for end to end speech recognition"
    - "unsupervised cross lingual representation learning at scale"
    - "camembert a tasty french language model"
    - "why deep transformers are difficult to converge from computation order to lipschitz restricted parameter initialization"

Connected through hyperedge HE-4 (induced by source paper "bertscore evaluating text generation with bert"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "fine tune bert for extractive summarization"
    - "alternative weighting schemes for elmo embeddings"
    - "deep reinforcement learning with distributional semantic rewards for abstractive summarization"
    - "pay less attention with lightweight and dynamic convolutions"
    - "simple applications of bert for ad hoc document retrieval"
    - "unifying human and statistical evaluation for natural language generation"
    - "scibert a pretrained language model for scientific text"

Connected through hyperedge HE-5 (induced by source paper "unlearn dataset bias in natural language inference by fitting the residual"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "selection bias explorations and debias methods for natural language sentence matching datasets"
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "don t take the premise for granted mitigating artifacts in natural language inference"
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "theoretically principled trade off between robustness and accuracy"
    - "learning robust representations by projecting superficial statistics out"
    - "gender bias in contextualized word embeddings"

Connected through hyperedge HE-6 (induced by source paper "on the cross lingual transferability of monolingual representations"):
  with members:
    - "multilingual alignment of contextual word representations"
    - "parameter efficient transfer learning for nlp"
    - "unicoder a universal language encoder by pre training with multiple cross lingual tasks"
    - "cross lingual ability of multilingual bert an empirical study"
    - "bilingual lexicon induction through unsupervised machine translation"
    - "wic the word in context dataset for evaluating context sensitive meaning representations"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "paws x a cross lingual adversarial dataset for paraphrase identification"

Connected through hyperedge HE-7 (induced by source paper "adversarial examples with difficult common words for paraphrase identification"):
  with members:
    - "paws x a cross lingual adversarial dataset for paraphrase identification"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "robust machine comprehension models via adversarial training"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "generating natural language adversarial examples"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "greedy attack and gumbel attack generating adversarial examples for discrete data"

Connected through hyperedge HE-8 (induced by source paper "automatic fact guided sentence modification"):
  with members:
    - "discofuse a large scale dataset for discourse based sentence fusion"
    - "bilinear attention networks"
    - "style transfer as unsupervised machine translation"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "allennlp a deep semantic natural language processing platform"
    - "fever a large scale dataset for fact extraction and verification"

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)