human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "cosmos qa machine reading comprehension with contextual commonsense reasoning"):
  Abstract: Understanding narratives requires reading between the lines, which in turn, requires interpreting the likely causes and effects of events, even when they are not mentioned explicitly. In this paper, we introduce Cosmos QA, a large-scale dataset of 35,600 problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. In stark contrast to most existing reading comprehension datasets where the questions focus on factual and literal understanding of the context paragraph, our dataset focuses on reading between the lines over a diverse collection of people's everyday narratives, asking such questions as "what might be the possible reason of ...?", or "what would have happened if ..." that require reasoning beyond the exact text spans in the context. To establish baseline performances on Cosmos QA, we experiment with several state-of-the-art neural architectures for reading comprehension, and also propose a new architecture that improves over the competitive baselines. Experimental results demonstrate a significant gap between machine (68.4%) and human performance (94%), pointing to avenues for future research on commonsense machine comprehension. Dataset, code and leaderboard is publicly available at this https URL.

Includes members:
  Member "bertscore evaluating text generation with bert":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on feature normalization and data augmentation")
  Member "socialiqa commonsense reasoning about social interactions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "enteval a holistic evaluation benchmark for entity representations")
  Member "dream a challenge dataset and models for dialogue based reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reclor a reading comprehension dataset requiring logical reasoning")
  Member "dual co matching network for multi choice reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the comparability of pre trained language models")
  Member "yuanfudao at semeval 2018 task 11 three way attention and relational knowledge for commonsense machine comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving question answering with external knowledge")
  Member "modeling naive psychology of characters in simple commonsense stories":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a knowledge enhanced pretraining model for commonsense story generation")
  Member "annotation artifacts in natural language inference data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial filters of dataset biases")
  Member "mcscript a novel dataset for assessing machine comprehension using script knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches")

Question: Please tell me which class the source paper 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)