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: "improving question answering by commonsense based pre training"
  Abstract: Although neural network approaches achieve remarkable success on a variety of NLP tasks, many of them struggle to answer questions that require commonsense knowledge. We believe the main reason is the lack of commonsense \mbox{connections} between concepts. To remedy this, we provide a simple and effective method that leverages external commonsense knowledge base such as ConceptNet. We pre-train direct and indirect relational functions between concepts, and show that these pre-trained functions could be easily added to existing neural network models. Results show that incorporating commonsense-based function improves the baseline on three question answering tasks that require commonsense reasoning. Further analysis shows that our system \mbox{discovers} and leverages useful evidence from an external commonsense knowledge base, which is missing in existing neural network models and help derive the correct answer.

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"
    - "kagnet knowledge aware graph networks for commonsense reasoning"
    - "driving datasets literature review"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-2 (induced by source paper "improving question answering with external knowledge"):
  with members:
    - "end to end open domain question answering with"
    - "yuanfudao at semeval 2018 task 11 three way attention and relational knowledge for commonsense machine comprehension"
    - "improving retrieval based question answering with deep inference models"
    - "ranking paragraphs for improving answer recall in open domain question answering"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "generating wikipedia by summarizing long sequences"
    - "what makes reading comprehension questions easier"
    - "kg 2 learning to reason science exam questions with contextual knowledge graph embeddings"

Connected through hyperedge HE-3 (induced by source paper "kepler a unified model for knowledge embedding and pre trained language representation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "graphvite a high performance cpu gpu hybrid system for node embedding"
    - "language models as knowledge bases"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "multi task deep neural networks for natural language understanding"
    - "matching the blanks distributional similarity for relation learning"

Connected through hyperedge HE-4 (induced by source paper "ernie enhanced language representation with informative entities"):
  with members:
    - "ernie enhanced representation through knowledge integration"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "joint representation learning of cross lingual words and entities via attentive distant supervision"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "ultra fine entity typing"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"

Connected through hyperedge HE-5 (induced by source paper "knowledge enhanced attention for robust natural language inference"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "annotation artifacts in natural language inference data"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (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"
    - "kagnet knowledge aware graph networks for commonsense reasoning"
    - "knowledge enhanced contextual word representations"
    - "dream a challenge dataset and models for dialogue based reading comprehension"

Connected through hyperedge HE-7 (induced by source paper "align mask and select a simple method for incorporating commonsense knowledge into language representation models"):
  with members:
    - "a surprisingly robust trick for the winograd schema challenge"
    - "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"
    - "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-8 (induced by source paper "kagnet knowledge aware graph networks for commonsense reasoning"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "does it make sense and why a pilot study for sense making and explanation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "from recognition to cognition visual commonsense reasoning"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "learning beyond datasets knowledge graph augmented neural networks for natural language processing"

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)