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: "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches"
  Abstract: Commonsense knowledge and commonsense reasoning are some of the main bottlenecks in machine intelligence. In the NLP community, many benchmark datasets and tasks have been created to address commonsense reasoning for language understanding. These tasks are designed to assess machines' ability to acquire and learn commonsense knowledge in order to reason and understand natural language text. As these tasks become instrumental and a driving force for commonsense research, this paper aims to provide an overview of existing tasks and benchmarks, knowledge resources, and learning and inference approaches toward commonsense reasoning for natural language understanding. Through this, our goal is to support a better understanding of the state of the art, its limitations, and future challenges.

Connected through hyperedge HE-1 (induced by source paper "a framework for evaluation of machine reading comprehension gold standards"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "quick and not so dirty unsupervised selection of justification sentences for multi hop question answering"
    - "compositional questions do not necessitate multi hop reasoning"
    - "question answering is a format when is it useful"
    - "understanding dataset design choices for multi hop reasoning"
    - "show your work improved reporting of experimental results"

Connected through hyperedge HE-2 (induced by source paper "towards a framework for visual intelligence in service robotics epistemic requirements and gap analysis"):
  with members:
    - "recent advances in natural language inference a survey of benchmarks resources and approaches"
    - "knowledge is never enough towards web aided deep open world recognition"
    - "continual lifelong learning with neural networks a review"
    - "a survey of knowledge representation in service robotics"
    - "integrating knowledge and reasoning in image understanding"
    - "aser a large scale eventuality knowledge graph"
    - "a review on intelligent object perception methods combining knowledge based reasoning and machine learning"
    - "visual semantic information pursuit a survey"

Connected through hyperedge HE-3 (induced by source paper "commonsense knowledge base completion with structural and semantic context"):
  with members:
    - "language models as knowledge bases"
    - "probing the need for visual context in multimodal machine translation"
    - "commonsense knowledge mining from pretrained models"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "answering science exam questions using query rewriting with background knowledge"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "commonsense mining as knowledge base completion a study on the impact of novelty"
    - "end to end structure aware convolutional networks for knowledge base completion"

Connected through hyperedge HE-4 (induced by source paper "improving neural story generation by targeted common sense grounding"):
  with members:
    - "the curious case of neural text degeneration"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "the natural language decathlon multitask learning as question answering"
    - "plan and write towards better automatic storytelling"
    - "deep contextualized word representations"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "commonsenseqa a question answering challenge targeting commonsense knowledge"

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)