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: "kagnet knowledge aware graph networks for commonsense reasoning"
  Abstract: Commonsense reasoning aims to empower machines with the human ability to make presumptions about ordinary situations in our daily life. In this paper, we propose a textual inference framework for answering commonsense questions, which effectively utilizes external, structured commonsense knowledge graphs to perform explainable inferences. The framework first grounds a question-answer pair from the semantic space to the knowledge-based symbolic space as a schema graph, a related sub-graph of external knowledge graphs. It represents schema graphs with a novel knowledge-aware graph network module named KagNet, and finally scores answers with graph representations. Our model is based on graph convolutional networks and LSTMs, with a hierarchical path-based attention mechanism. The intermediate attention scores make it transparent and interpretable, which thus produce trustworthy inferences. Using ConceptNet as the only external resource for Bert-based models, we achieved state-of-the-art performance on the CommonsenseQA, a large-scale dataset for commonsense reasoning.

Connected through hyperedge HE-1 (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"
    - "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-2 (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"
    - "levenshtein transformer"
    - "text infilling"

Connected through hyperedge HE-3 (induced by source paper "towards generalizable neuro symbolic systems for commonsense question answering"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "improving question answering with external knowledge"
    - "augmenting neural networks with first order logic"
    - "option comparison network for multiple choice reading comprehension"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "knowledge enhanced contextual word representations"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "sensebert driving some sense into bert"

Connected through hyperedge HE-4 (induced by source paper "graph based reasoning over heterogeneous external knowledge for commonsense question answering"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "explore propose and assemble an interpretable model for multi hop reading comprehension"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "align mask and select a simple method for incorporating commonsense knowledge into language representation models"
    - "roberta a robustly optimized bert pretraining approach"
    - "mcscript2 0 a machine comprehension corpus focused on script events and participants"

Connected through hyperedge HE-5 (induced by source paper "dynamic knowledge graph construction for zero shot commonsense question answering"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "explore propose and assemble an interpretable model for multi hop reading comprehension"
    - "using local knowledge graph construction to scale seq2seq models to multi document inputs"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "self assembling modular networks for interpretable multi hop reasoning"
    - "ranking and selecting multi hop knowledge paths to better predict human needs"
    - "modeling naive psychology of characters in simple commonsense stories"
    - "learning to write with cooperative discriminators"

Connected through hyperedge HE-6 (induced by source paper "teaching pretrained models with commonsense reasoning a preliminary kb based approach"):
  with members:
    - "incorporating external knowledge into machine reading for generative question answering"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cosmos qa machine reading comprehension with contextual commonsense reasoning"
    - "align mask and select a simple method for incorporating commonsense knowledge into language representation models"
    - "yuanfudao at semeval 2018 task 11 three way attention and relational knowledge for commonsense machine comprehension"
    - "improving question answering by commonsense based pre training"
    - "a simple method for commonsense reasoning"

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)