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 "textbook question answering with knowledge graph understanding and unsupervised open set text comprehension"):
  Abstract: In this work, we introduce a novel algorithm for solving the textbook question answering (TQA) task which describes more realistic QA problems compared to other recent tasks. We mainly focus on two related issues with analysis of TQA dataset. First, it requires to comprehend long lessons to extract knowledge. To tackle this issue of extracting knowledge features from long lessons, we establish knowledge graph from texts and incorporate graph convolutional network (GCN). Second, scientific terms are not spread over the chapters and data splits in TQA dataset. To overcome this so called `out-of-domain' issue, we add novel unsupervised text learning process without any annotations before learning QA problems. The experimental results show that our model significantly outperforms prior state-of-the-art methods. Moreover, ablation studies validate that both methods of incorporating GCN for extracting knowledge from long lessons and our newly proposed unsupervised learning process are meaningful to solve this problem.

Includes members:
  Member "learning conditioned graph structures for interpretable visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cq vqa visual question answering on categorized questions")
  Member "dynamic graph generation network generating relational knowledge from diagrams":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transfer learning in visual and relational reasoning")
  Member "multi modal factorized bilinear pooling with co attention learning for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "bidirectional attention flow for machine comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "description based text classification with reinforcement learning")
  Member "a diagram is worth a dozen images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "plotqa reasoning over scientific plots")
  Member "squad 100 000 questions for machine comprehension of text":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "graph structured representations for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "ms marco a human generated machine reading comprehension dataset":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a study of the tasks and models in machine reading comprehension")

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)