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: "commonsenseqa a question answering challenge targeting commonsense knowledge"
  Abstract: When answering a question, people often draw upon their rich world knowledge in addition to some task-specific context. Recent work has focused primarily on answering questions based on some relevant document or content, and required very little general background. To investigate question answering with prior knowledge, we present CommonsenseQA: a difficult new dataset for commonsense question answering. To capture common sense beyond associations, each question discriminates between three target concepts that all share the same relationship to a single source drawn from ConceptNet (Speer et al., 2017). This constraint encourages crowd workers to author multiple-choice questions with complex semantics, in which all candidates relate to the subject in a similar way. We create 9,500 questions through this procedure and demonstrate the dataset's difficulty with a large number of strong baselines. Our best baseline, the OpenAI GPT (Radford et al., 2018), obtains 54.8% accuracy, well below human performance, which is 95.3%.

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 "freelb enhanced adversarial training for natural language understanding"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "adversarial attacks on copyright detection systems"
    - "multi task deep neural networks for natural language understanding"
    - "albert a lite bert for self supervised learning of language representations"
    - "robust neural machine translation with doubly adversarial inputs"
    - "roberta a robustly optimized bert pretraining approach"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"

Connected through hyperedge HE-3 (induced by source paper "jiant a software toolkit for research on general purpose text understanding models"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "bert rediscovers the classical nlp pipeline"
    - "multi task deep neural networks for natural language understanding"
    - "open sesame getting inside bert s linguistic knowledge"
    - "albert a lite bert for self supervised learning of language representations"
    - "probing what different nlp tasks teach machines about function word comprehension"

Connected through hyperedge HE-4 (induced by source paper "neuro symbolic architectures for context understanding"):
  with members:
    - "an evaluation of knowledge graph embeddings for autonomous driving data experience and practice"
    - "multi task feature learning for knowledge graph enhanced recommendation"
    - "option comparison network for multiple choice reading comprehension"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "knowledge graph embedding for ecotoxicological effect prediction"
    - "kagnet knowledge aware graph networks for commonsense reasoning"
    - "driving datasets literature review"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-5 (induced by source paper "joint reasoning for multi faceted commonsense knowledge"):
  with members:
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "embedding uncertain knowledge graphs"
    - "atomic an atlas of machine commonsense for if then reasoning"
    - "engaging image captioning via personality"
    - "moving beyond the turing test with the allen ai science challenge"
    - "vqa visual question answering"
    - "incremental knowledge base construction using deepdive"

Connected through hyperedge HE-6 (induced by source paper "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "multi task deep neural networks for natural language understanding"
    - "explicit bias discovery in visual question answering models"
    - "modeling naive psychology of characters in simple commonsense stories"
    - "from recognition to cognition visual commonsense reasoning"
    - "annotation artifacts in natural language inference data"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "gender bias in coreference resolution"

Connected through hyperedge HE-7 (induced by source paper "enteval a holistic evaluation benchmark for entity representations"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "joint learning of named entity recognition and entity linking"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "evaluation benchmarks and learning criteriafor discourse aware sentence representations"
    - "ernie enhanced representation through knowledge integration"
    - "zero shot entity linking by reading entity descriptions"
    - "socialiqa commonsense reasoning about social interactions"
    - "seeing things from a different angle discovering diverse perspectives about claims"

Connected through hyperedge HE-8 (induced by source paper "commongen a constrained text generation dataset towards generative commonsense reasoning"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "codah an adversarially authored question answer dataset for common sense"
    - "bertscore evaluating text generation with bert"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "vatex a large scale high quality multilingual dataset for video and language research"
    - "mirrorgan learning text to image generation by redescription"
    - "kagnet knowledge aware graph networks for commonsense reasoning"
    - "levenshtein transformer"

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)