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 "i know what you want semantic learning for text comprehension"):
  Abstract: Who did what to whom is a major focus in natural language understanding, which is right the aim of semantic role labeling (SRL). Although SRL is naturally essential to text comprehension tasks, it is surprisingly ignored in previous work. This paper thus makes the first attempt to let SRL enhance text comprehension and inference through specifying verbal arguments and their corresponding semantic roles. In terms of deep learning models, our embeddings are enhanced by semantic role labels for more fine-grained semantics. We show that the salient labels can be conveniently added to existing models and significantly improve deep learning models in challenging text comprehension tasks. Extensive experiments on benchmark machine reading comprehension and inference datasets verify that the proposed semantic learning helps our system reach new state-of-the-art.

Includes members:
  Member "discourse marker augmented network with reinforcement learning for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bi decoder augmented network for neural machine translation")
  Member "multi granularity hierarchical attention fusion networks for reading comprehension and question answering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "know what you don t know unanswerable questions for squad":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "description based text classification with reinforcement learning")
  Member "jointly predicting predicates and arguments in neural semantic role labeling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spanbert improving pre training by representing and predicting spans")
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "subword augmented embedding for cloze reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sg net syntax guided machine reading comprehension")
  Member "one shot learning for question answering in gaokao history challenge":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sg net syntax guided machine reading comprehension")
  Member "large scale qa srl parsing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "question answering is a format when is it useful")

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)