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: "improvised comedy as a turing test"
  Abstract: The best improvisational theatre actors can make any scene partner, of any skill level or ability, appear talented and proficient in the art form, and thus "make them shine". To challenge this improvisational paradigm, we built an artificial intelligence (AI) trained to perform live shows alongside human actors for human audiences. Over the course of 30 performances to a combined audience of almost 3000 people, we have refined theatrical games which involve combinations of human and (at times, adversarial) AI actors. We have developed specific scene structures to include audience participants in interesting ways. Finally, we developed a complete show structure that submitted the audience to a Turing test and observed their suspension of disbelief, which we believe is key for human/non-human theatre co-creation.

Connected through hyperedge HE-1 (induced by source paper "shaping the narrative arc an information theoretic approach to collaborative dialogue"):
  with members:
    - "predicting movie genres based on plot summaries"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "generating wikipedia by summarizing long sequences"
    - "smart conversational agents for reminiscence"
    - "universal sentence encoder"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "toward controlled generation of text"
    - "generating sentences by editing prototypes"

Connected through hyperedge HE-2 (induced by source paper "augmenting neural response generation with context aware topical attention"):
  with members:
    - "universal sentence encoder"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "topic aware neural response generation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"

Connected through hyperedge HE-3 (induced by source paper "improbotics exploring the imitation game using machine intelligence in improvised theatre"):
  with members:
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "generating sentences by editing prototypes"
    - "automated hate speech detection and the problem of offensive language"
    - "event representations for automated story generation with deep neural nets"
    - "a neural conversational model"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-4 (induced by source paper "dairector automatic story beat generation through knowledge synthesis"):
  with members:
    - "universal sentence encoder"
    - "event representations for automated story generation with deep neural nets"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

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.AI(Artificial Intelligence)