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: "m2d monolog to dialog generation for conversational story telling"
  Abstract: Storytelling serves many different social functions, e.g. stories are used to persuade, share troubles, establish shared values, learn social behaviors, and entertain. Moreover, stories are often told conversationally through dialog, and previous work suggests that information provided dialogically is more engaging than when provided in monolog. In this paper, we present algorithms for converting a deep representation of a story into a dialogic storytelling, that can vary aspects of the telling, including the personality of the storytellers. We conduct several experiments to test whether dialogic storytellings are more engaging, and whether automatically generated variants in linguistic form that correspond to personality differences can be recognized in an extended storytelling dialog.

Connected through hyperedge HE-1 (induced by source paper "slugbot developing a computational model andframework of a novel dialogue genre"):
  with members:
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "slugbot an application of a novel and scalable open domain socialbot framework"
    - "slugnerds a named entity recognition tool for open domain dialogue systems"
    - "attention is all you need"
    - "combining search with structured data to create a more engaging user experience in open domain dialogue"
    - "edina building an open domain socialbot with self dialogues"
    - "personabank a corpus of personal narratives and their story intention graphs"

Connected through hyperedge HE-2 (induced by source paper "plan and write towards better automatic storytelling"):
  with members:
    - "story ending generation with incremental encoding and commonsense knowledge"
    - "a skeleton based model for promoting coherence among sentences in narrative story generation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "hierarchical neural story generation"
    - "event representations for automated story generation with deep neural nets"
    - "regularizing and optimizing lstm language models"
    - "story generation from sequence of independent short descriptions"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-3 (induced by source paper "slugbot an application of a novel and scalable open domain socialbot framework"):
  with members:
    - "personabank a corpus of personal narratives and their story intention graphs"

Connected through hyperedge HE-4 (induced by source paper "data driven dialogue systems for social agents"):
  with members:
    - "automating direct speech variations in stories and games"
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "a persona based neural conversation model"
    - "measuring the similarity of sentential arguments in dialogue"
    - "improved deep learning baselines for ubuntu corpus dialogs"
    - "a survey of available corpora for building data driven dialogue systems"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "and that s a fact distinguishing factual and emotional argumentation in online dialogue"

Connected through hyperedge HE-5 (induced by source paper "debbie the debate bot of the future"):
  with members:
    - "topic independent identification of agreement and disagreement in social media dialogue"
    - "argument strength is in the eye of the beholder audience effects in persuasion"
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "measuring the similarity of sentential arguments in dialogue"
    - "neural network based abstract generation for opinions and arguments"
    - "and that s a fact distinguishing factual and emotional argumentation in online dialogue"
    - "using summarization to discover argument facets in online idealogical dialog"

Connected through hyperedge HE-6 (induced by source paper "combining search with structured data to create a more engaging user experience in open domain dialogue"):
  with members:
    - "automatically training a problematic dialogue predictor for a spoken dialogue system"
    - "an application of reinforcement learning to dialogue strategy selection in a spoken dialogue system for email"

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)