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 "incorporating relation knowledge into commonsense reading comprehension with multi task learning"):
  Abstract: This paper focuses on how to take advantage of external relational knowledge to improve machine reading comprehension (MRC) with multi-task learning. Most of the traditional methods in MRC assume that the knowledge used to get the correct answer generally exists in the given documents. However, in real-world task, part of knowledge may not be mentioned and machines should be equipped with the ability to leverage external knowledge. In this paper, we integrate relational knowledge into MRC model for commonsense reasoning. Specifically, based on a pre-trained language model (LM), We design two auxiliary relation-aware tasks to predict if there exists any commonsense relation and what is the relation type be-tween two words, in order to better model the interactions between document and candidate answer option. We conduct experiments on two multi-choice benchmark datasets: the SemEval-2018 Task11 and the Cloze Story Test. The experimental results demonstrate the effectiveness of the proposed method, which achieves superior performance compared with the comparable baselines on both datasets.

Includes members:
  Member "leveraging knowledge bases in lstms for improving machine reading":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "differentiable reasoning over a virtual knowledge base")
  Member "yuanfudao at semeval 2018 task 11 three way attention and relational knowledge for commonsense machine comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving question answering with external knowledge")
  Member "improving question answering by commonsense based pre training":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neuro symbolic architectures for context understanding")
  Member "multi granularity hierarchical attention fusion networks for reading comprehension and question answering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "hfl rc system at semeval 2018 task 11 hybrid multi aspects model for commonsense reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convolutional spatial attention model for reading comprehension with multiple choice questions")
  Member "qanet combining local convolution with global self attention for reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on feature normalization and data augmentation")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "commonsense for generative multi hop question answering tasks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neuro symbolic architectures for context understanding")

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)