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: "comet commonsense transformers for automatic knowledge graph construction"
  Abstract: We present the first comprehensive study on automatic knowledge base construction for two prevalent commonsense knowledge graphs: ATOMIC (Sap et al., 2019) and ConceptNet (Speer et al., 2017). Contrary to many conventional KBs that store knowledge with canonical templates, commonsense KBs only store loosely structured open-text descriptions of knowledge. We posit that an important step toward automatic commonsense completion is the development of generative models of commonsense knowledge, and propose COMmonsEnse Transformers (COMET) that learn to generate rich and diverse commonsense descriptions in natural language. Despite the challenges of commonsense modeling, our investigation reveals promising results when implicit knowledge from deep pre-trained language models is transferred to generate explicit knowledge in commonsense knowledge graphs. Empirical results demonstrate that COMET is able to generate novel knowledge that humans rate as high quality, with up to 77.5% (ATOMIC) and 91.7% (ConceptNet) precision at top 1, which approaches human performance for these resources. Our findings suggest that using generative commonsense models for automatic commonsense KB completion could soon be a plausible alternative to extractive methods.

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 "a knowledge enhanced pretraining model for commonsense story generation"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "fine tuning pre trained transformer language models to distantly supervised relation extraction"
    - "long and diverse text generation with planning based hierarchical variational model"
    - "the curious case of neural text degeneration"
    - "pretraining methods for dialog context representation learning"
    - "ernie enhanced representation through knowledge integration"
    - "do massively pretrained language models make better storytellers"
    - "mass masked sequence to sequence pre training for language generation"

Connected through hyperedge HE-3 (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"
    - "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"
    - "dream a challenge dataset and models for dialogue based reading comprehension"

Connected through hyperedge HE-4 (induced by source paper "video2commonsense generating commonsense descriptions to enrich video captioning"):
  with members:
    - "vqa lol visual question answering under the lens of logic"
    - "procedure planning in instructional videos"
    - "lxmert learning cross modality encoder representations from transformers"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "blocksworld revisited learning and reasoning to generate event sequences from image pairs"
    - "roberta a robustly optimized bert pretraining approach"
    - "from recognition to cognition visual commonsense reasoning"
    - "end to end dense video captioning with masked transformer"

Connected through hyperedge HE-5 (induced by source paper "mining disinformation and fake news concepts methods and recent advancements"):
  with members:
    - "plug and play language models a simple approach to controlled text generation"
    - "generalizing from a few examples a survey on few shot learning"
    - "ctrl a conditional transformer language model for controllable generation"
    - "release strategies and the social impacts of language models"
    - "defending against neural fake news"
    - "studying fake news via network analysis detection and mitigation"
    - "hierarchical propagation networks for fake news detection investigation and exploitation"
    - "declare debunking fake news and false claims using evidence aware deep learning"

Connected through hyperedge HE-6 (induced by source paper "joint reasoning for multi faceted commonsense knowledge"):
  with members:
    - "explain yourself leveraging language models for commonsense reasoning"
    - "embedding uncertain knowledge graphs"
    - "commonsenseqa a question answering challenge targeting commonsense knowledge"
    - "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-7 (induced by source paper "on the role of conceptualization in commonsense knowledge graph construction"):
  with members:
    - "kg bert bert for knowledge graph completion"
    - "aser a large scale eventuality knowledge graph"
    - "roberta a robustly optimized bert pretraining approach"
    - "a simple method for commonsense reasoning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end structure aware convolutional networks for knowledge base completion"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-8 (induced by source paper "huggingface s transformers state of the art natural language processing"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "are sixteen heads really better than one"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "bert rediscovers the classical nlp pipeline"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "the curious case of neural text degeneration"

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)