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: "shaping the narrative arc an information theoretic approach to collaborative dialogue"
  Abstract: We consider the problem of designing an artificial agent capable of interacting with humans in collaborative dialogue to produce creative, engaging narratives. In this task, the goal is to establish universe details, and to collaborate on an interesting story in that universe, through a series of natural dialogue exchanges. Our model can augment any probabilistic conversational agent by allowing it to reason about universe information established and what potential next utterances might reveal. Ideally, with each utterance, agents would reveal just enough information to add specificity and reduce ambiguity without limiting the conversation. We empirically show that our model allows control over the rate at which the agent reveals information and that doing so significantly improves accuracy in predicting the next line of dialogues from movies. We close with a case-study with four professional theatre performers, who preferred interactions with our model-augmented agent over an unaugmented agent.

Connected through hyperedge HE-1 (induced by source paper "sentence level content planning and style specification for neural text generation"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "argument generation with retrieval planning and realization"
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "step by step separating planning from realization in neural data to text generation"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "neural argument generation augmented with externally retrieved evidence"
    - "hierarchical neural story generation"
    - "a graph to sequence model for amr to text generation"

Connected through hyperedge HE-2 (induced by source paper "constrained decoding for neural nlg from compositional representations in task oriented dialogue"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "step by step separating planning from realization in neural data to text generation"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "constituency parsing with a self attentive encoder"
    - "findings of the e2e nlg challenge"
    - "data to text generation with content selection and planning"

Connected through hyperedge HE-3 (induced by source paper "latent space factorisation and manipulation via matrix subspace projection"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "deep nets what have they ever done for vision"
    - "learning latent subspaces in variational autoencoders"
    - "i revnet deep invertible networks"
    - "dirichlet variational autoencoder for text modeling"
    - "toward controlled generation of text"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "cvae gan fine grained image generation through asymmetric training"

Connected through hyperedge HE-4 (induced by source paper "performing structured improvisations with pre trained deep learning models"):
  with members:
    - "a hierarchical latent vector model for learning long term structure in music"
    - "everybody dance now"
    - "attention is all you need"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "high resolution image synthesis and semantic manipulation with conditional gans"

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.HC(Human-Computer Interaction)