human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "coherence models for dialogue"
  Abstract: Coherence across multiple turns is a major challenge for state-of-the-art dialogue models. Arguably the most successful approach to automatically learning text coherence is the entity grid, which relies on modelling patterns of distribution of entities across multiple sentences of a text. Originally applied to the evaluation of automatic summaries and the news genre, among its many extensions, this model has also been successfully used to assess dialogue coherence. Nevertheless, both the original grid and its extensions do not model intents, a crucial aspect that has been studied widely in the literature in connection to dialogue structure. We propose to augment the original grid document representation for dialogue with the intentional structure of the conversation. Our models outperform the original grid representation on both text discrimination and insertion, the two main standard tasks for coherence assessment across three different dialogue datasets, confirming that intents play a key role in modelling dialogue coherence.

Connected through hyperedge HE-1 (induced by source paper "towards coherent and engaging spoken dialog response generation using automatic conversation evaluators"):
  with members:
    - "learning to write with cooperative discriminators"
    - "topic based evaluation for conversational bots"
    - "an efficient framework for learning sentence representations"
    - "contextual topic modeling for dialog systems"
    - "conversational ai the science behind the alexa prize"
    - "on evaluating and comparing open domain dialog systems"
    - "attention is all you need"
    - "ruber an unsupervised method for automatic evaluation of open domain dialog systems"

Connected through hyperedge HE-2 (induced by source paper "a neural model for dialogue coherence assessment"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "reward learning for efficient reinforcement learning in extractive document summarisation"
    - "multi task learning for coherence modeling"
    - "evaluating coherence in dialogue systems using entailment"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "why comparing single performance scores does not allow to draw conclusions about machine learning approaches"
    - "measuring semantic coherence of a conversation"

Question: Please tell me which class the center node 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)