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: "winogrande an adversarial winograd schema challenge at scale"
  Abstract: The Winograd Schema Challenge (WSC) (Levesque, Davis, and Morgenstern 2011), a benchmark for commonsense reasoning, is a set of 273 expert-crafted pronoun resolution problems originally designed to be unsolvable for statistical models that rely on selectional preferences or word associations. However, recent advances in neural language models have already reached around 90% accuracy on variants of WSC. This raises an important question whether these models have truly acquired robust commonsense capabilities or whether they rely on spurious biases in the datasets that lead to an overestimation of the true capabilities of machine commonsense. To investigate this question, we introduce WinoGrande, a large-scale dataset of 44k problems, inspired by the original WSC design, but adjusted to improve both the scale and the hardness of the dataset. The key steps of the dataset construction consist of (1) a carefully designed crowdsourcing procedure, followed by (2) systematic bias reduction using a novel AfLite algorithm that generalizes human-detectable word associations to machine-detectable embedding associations. The best state-of-the-art methods on WinoGrande achieve 59.4-79.1%, which are 15-35% below human performance of 94.0%, depending on the amount of the training data allowed. Furthermore, we establish new state-of-the-art results on five related benchmarks - WSC (90.1%), DPR (93.1%), COPA (90.6%), KnowRef (85.6%), and Winogender (97.1%). These results have dual implications: on one hand, they demonstrate the effectiveness of WinoGrande when used as a resource for transfer learning. On the other hand, they raise a concern that we are likely to be overestimating the true capabilities of machine commonsense across all these benchmarks. We emphasize the importance of algorithmic bias reduction in existing and future benchmarks to mitigate such overestimation.

Connected through hyperedge HE-1 (induced by source paper "natural language qa approaches using reasoning with external knowledge"):
  with members:
    - "transformers as soft reasoners over language"
    - "recent advances in natural language inference a survey of benchmarks resources and approaches"
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "piqa reasoning about physical commonsense in natural language"
    - "language models as knowledge bases"
    - "quoref a reading comprehension dataset with questions requiring coreferential reasoning"
    - "understanding roles and entities datasets and models for natural language inference"
    - "qasc a dataset for question answering via sentence composition"

Connected through hyperedge HE-2 (induced by source paper "adversarial filters of dataset biases"):
  with members:
    - "dataset distillation"
    - "hellaswag can a machine really finish your sentence"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "repair removing representation bias by dataset resampling"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "natural adversarial examples"
    - "randaugment practical automated data augmentation with a reduced search space"

Connected through hyperedge HE-3 (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"
    - "paws paraphrase adversaries from word scrambling"
    - "theoretically principled trade off between robustness and accuracy"
    - "learning robust representations by projecting superficial statistics out"

Connected through hyperedge HE-4 (induced by source paper "piqa reasoning about physical commonsense in natural language"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "language models as knowledge bases"
    - "assessing bert s syntactic abilities"
    - "tool macgyvering tool construction using geometric reasoning"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "hake human activity knowledge engine"
    - "roberta a robustly optimized bert pretraining approach"
    - "how large are lions inducing distributions over quantitative attributes"

Connected through hyperedge HE-5 (induced by source paper "what do deep networks like to read"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "interpretable neural predictions with differentiable binary variables"
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"
    - "analysis methods in neural language processing a survey"
    - "what do deep networks like to see"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "performance impact caused by hidden bias of training data for recognizing textual entailment"

Connected through hyperedge HE-6 (induced by source paper "can a gorilla ride a camel learning semantic plausibility from text"):
  with members:
    - "universal dependency parsing from scratch"
    - "solving hard coreference problems"
    - "sp 10k a large scale evaluation set for selectional preference acquisition"
    - "exploring unsupervised pretraining and sentence structure modelling for winograd schema challenge"
    - "do neural language representations learn physical commonsense"
    - "probing neural network comprehension of natural language arguments"
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"

Connected through hyperedge HE-7 (induced by source paper "socialiqa commonsense reasoning about social interactions"):
  with members:
    - "a surprisingly robust trick for the winograd schema challenge"
    - "solving hard coreference problems"
    - "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 "social bias frames reasoning about social and power implications of language"):
  with members:
    - "talkdown a corpus for condescension detection in context"
    - "unifying human and statistical evaluation for natural language generation"
    - "model cards for model reporting"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "contextual affective analysis a case study of people portrayals in online metoo stories"
    - "cooperative generator discriminator networks for abstractive summarization with narrative flow"
    - "predicting the type and target of offensive posts in social media"
    - "racial bias in hate speech and abusive language detection datasets"

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)