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 "graphflow exploiting conversation flow with graph neural networks for conversational machine comprehension"):
  Abstract: Conversational machine reading comprehension (MRC) has proven significantly more challenging compared to traditional MRC since it requires better utilization of conversation history. However, most existing approaches do not effectively capture conversation history and thus have trouble handling questions involving coreference or ellipsis. We propose a novel graph neural network (GNN) based model, namely GraphFlow, which captures conversational flow in the dialog. Specifically, we first propose a new approach to dynamically construct a question-aware context graph from passage text at each turn. We then present a novel flow mechanism to model the temporal dependencies in the sequence of context graphs. The proposed GraphFlow model shows superior performance compared to existing state-of-the-art methods. For instance, GraphFlow outperforms two recently proposed models on the CoQA benchmark dataset: FlowQA by 2.3% and SDNet by 0.7% on F1 score, respectively. In addition, visualization experiments show that our proposed model can better mimic the human reasoning process for conversational MRC compared to existing models.

Includes members:
  Member "sdnet contextualized attention based deep network for conversational question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ma dst multi attention based scalable dialog state tracking")
  Member "sql to text generation with graph to sequence model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reinforcement learning based graph to sequence model for natural question generation")
  Member "multi granularity hierarchical attention fusion networks for reading comprehension and question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "a qualitative comparison of coqa squad 2 0 and quac":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a framework for evaluation of machine reading comprehension gold standards")
  Member "exploiting rich syntactic information for semantic parsing with graph to sequence model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reinforcement learning based graph to sequence model for natural question generation")
  Member "contextualized non local neural networks for sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reinforcement learning based graph to sequence model for natural question generation")
  Member "coqa a conversational question answering challenge":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")
  Member "graph2seq graph to sequence learning with attention based neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "towards graph representation learning in emergent communication")

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)