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 "memorizing all for implicit discourse relation recognition"):
  Abstract: Implicit discourse relation recognition is a challenging task due to the absence of the necessary informative clue from explicit connectives. The prediction of relations requires a deep understanding of the semantic meanings of sentence pairs. As implicit discourse relation recognizer has to carefully tackle the semantic similarity of the given sentence pairs and the severe data sparsity issue exists in the meantime, it is supposed to be beneficial from mastering the entire training data. Thus in this paper, we propose a novel memory mechanism to tackle the challenges for further performance improvement. The memory mechanism is adequately memorizing information by pairing representations and discourse relations of all training instances, which right fills the slot of the data-hungry issue in the current implicit discourse relation recognizer. Our experiments show that our full model with memorizing the entire training set reaches new state-of-the-art against strong baselines, which especially for the first time exceeds the milestone of 60% accuracy in the 4-way task.

Includes members:
  Member "deep enhanced representation for implicit discourse relation recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised adversarial domain adaptation for implicit discourse relation classification")
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "improving implicit discourse relation classification by modeling inter dependencies of discourse units in a paragraph":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised adversarial domain adaptation for implicit discourse relation classification")
  Member "a recurrent neural model with attention for the recognition of chinese implicit discourse relations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semantic graph convolutional network for implicit discourse relation classification")
  Member "implicit discourse relation classification via multi task neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised adversarial domain adaptation for implicit discourse relation classification")
  Member "deep biaffine attention for neural dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lost in embedding space explaining cross lingual task performance with eigenvalue divergence")
  Member "language modeling with gated convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "contextual lensing of universal sentence representations")
  Member "key value memory networks for directly reading documents":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attention over parameters for dialogue systems")

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)