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 "a neural model for dialogue coherence assessment"):
  Abstract: Dialogue quality assessment is crucial for evaluating dialogue agents. An essential factor of high-quality dialogues is coherence - what makes dialogue utterances a whole. This paper proposes a novel dialogue coherence model trained in a hierarchical multi-task learning scenario where coherence assessment is the primary and the high-level task, and dialogue act prediction is the auxiliary and the low-level task. The results of our experiments for two benchmark dialogue corpora (i.e. SwitchBoard and DailyDialog) show that our model significantly outperforms its competitors for ranking dialogues with respect to their coherence. Although the performance of other examined models considerably varies across examined corpora, our model robustly achieves high performance. We release the source code and datasets defined for the experiments in this paper to accelerate future research on dialogue coherence.

Includes members:
  Member "the second conversational intelligence challenge convai2":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attention over parameters for dialogue systems")
  Member "reward learning for efficient reinforcement learning in extractive document summarisation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fine tuning language models from human preferences")
  Member "multi task learning for coherence modeling":
    also connected through other hyperedges:
      (none)
  Member "evaluating coherence in dialogue systems using entailment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "studying the effects of cognitive biases in evaluation of conversational agents")
  Member "what makes a good conversation how controllable attributes affect human judgments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "xpersona evaluating multilingual personalized chatbot")
  Member "better conversations by modeling filtering and optimizing for coherence and diversity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "posterior gan towards informative and coherent response generation with posterior generative adversarial network")
  Member "why comparing single performance scores does not allow to draw conclusions about machine learning approaches":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "combining visual and textual features for semantic segmentation of historical newspapers")
  Member "measuring semantic coherence of a conversation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the technological gap between virtual assistants and recommendation 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)