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 "modeling multi turn conversation with deep utterance aggregation"):
  Abstract: Multi-turn conversation understanding is a major challenge for building intelligent dialogue systems. This work focuses on retrieval-based response matching for multi-turn conversation whose related work simply concatenates the conversation utterances, ignoring the interactions among previous utterances for context modeling. In this paper, we formulate previous utterances into context using a proposed deep utterance aggregation model to form a fine-grained context representation. In detail, a self-matching attention is first introduced to route the vital information in each utterance. Then the model matches a response with each refined utterance and the final matching score is obtained after attentive turns aggregation. Experimental results show our model outperforms the state-of-the-art methods on three multi-turn conversation benchmarks, including a newly introduced e-commerce dialogue corpus.

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 "subword augmented embedding for cloze reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "sg net syntax guided machine reading comprehension")
  Member "personalizing dialogue agents i have a dog do you have pets too":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attention over parameters for dialogue systems")
  Member "sjtu nlp at semeval 2018 task 9 neural hypernym discovery with term embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "effective subword segmentation for text comprehension")
  Member "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "crwiz a framework for crowdsourcing real time wizard of oz dialogues")
  Member "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "fast and accurate neural word segmentation for chinese":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "glyce glyph vectors for chinese character representations")

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)