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: "a surprisingly robust trick for the winograd schema challenge"
  Abstract: The Winograd Schema Challenge (WSC) dataset WSC273 and its inference counterpart WNLI are popular benchmarks for natural language understanding and commonsense reasoning. In this paper, we show that the performance of three language models on WSC273 strongly improves when fine-tuned on a similar pronoun disambiguation problem dataset (denoted WSCR). We additionally generate a large unsupervised WSC-like dataset. By fine-tuning the BERT language model both on the introduced and on the WSCR dataset, we achieve overall accuracies of 72.5% and 74.7% on WSC273 and WNLI, improving the previous state-of-the-art solutions by 8.8% and 9.6%, respectively. Furthermore, our fine-tuned models are also consistently more robust on the "complex" subsets of WSC273, introduced by Trichelair et al. (2018).

Connected through hyperedge HE-1 (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"
    - "paws paraphrase adversaries from word scrambling"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "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-2 (induced by source paper "roberta a robustly optimized bert pretraining approach"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "ernie enhanced representation through knowledge integration"
    - "spanbert improving pre training by representing and predicting spans"
    - "mass masked sequence to sequence pre training for language generation"
    - "multi task deep neural networks for natural language understanding"
    - "cloze driven pretraining of self attention networks"
    - "defending against neural fake news"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"

Connected through hyperedge HE-3 (induced by source paper "xlnet generalized autoregressive pretraining for language understanding"):
  with members:
    - "improving question answering with external knowledge"
    - "option comparison network for multiple choice reading comprehension"
    - "multi task deep neural networks for natural language understanding"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "unsupervised data augmentation for consistency training"
    - "roberta a robustly optimized bert pretraining approach"
    - "dual co matching network for multi choice reading comprehension"

Connected through hyperedge HE-4 (induced by source paper "smart robust and efficient fine tuning for pre trained natural language models through principled regularized optimization"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "parameter efficient transfer learning for nlp"
    - "spanbert improving pre training by representing and predicting spans"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "on the variance of the adaptive learning rate and beyond"
    - "multi task deep neural networks for natural language understanding"
    - "albert a lite bert for self supervised learning of language representations"
    - "theoretically principled trade off between robustness and accuracy"

Connected through hyperedge HE-5 (induced by source paper "align mask and select a simple method for incorporating commonsense knowledge into language representation models"):
  with members:
    - "ernie enhanced language representation with informative entities"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "exploring unsupervised pretraining and sentence structure modelling for winograd schema challenge"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "improving question answering by commonsense based pre training"
    - "how reasonable are common sense reasoning tasks a case study on the winograd schema challenge and swag"
    - "a simple method for commonsense reasoning"

Connected through hyperedge HE-6 (induced by source paper "winogrande an adversarial winograd schema challenge at scale"):
  with members:
    - "solving hard coreference problems"
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"
    - "a hybrid neural network model for commonsense reasoning"
    - "probing neural network comprehension of natural language arguments"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "roberta a robustly optimized bert pretraining approach"
    - "on adversarial removal of hypothesis only bias in natural language inference"
    - "multinomial adversarial networks for multi domain text classification"

Connected through hyperedge HE-7 (induced by source paper "socialiqa commonsense reasoning about social interactions"):
  with members:
    - "solving hard coreference problems"
    - "winogrande an adversarial winograd schema challenge at scale"
    - "from recognition to cognition visual commonsense reasoning"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "annotation artifacts in natural language inference data"
    - "evaluating theory of mind in question answering"
    - "machine common sense concept paper"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-8 (induced by source paper "aser a large scale eventuality knowledge graph"):
  with members:
    - "joint reasoning for temporal and causal relations"
    - "sp 10k a large scale evaluation set for selectional preference acquisition"
    - "how reasonable are common sense reasoning tasks a case study on the winograd schema challenge and swag"
    - "a simple method for commonsense reasoning"
    - "on the evaluation of common sense reasoning in natural language understanding"
    - "a knowledge hunting framework for common sense reasoning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)