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: "on how users edit computer generated visual stories"
  Abstract: A significant body of research in Artificial Intelligence (AI) has focused on generating stories automatically, either based on prior story plots or input images. However, literature has little to say about how users would receive and use these stories. Given the quality of stories generated by modern AI algorithms, users will nearly inevitably have to edit these stories before putting them to real use. In this paper, we present the first analysis of how human users edit machine-generated stories. We obtained 962 short stories generated by one of the state-of-the-art visual storytelling models. For each story, we recruited five crowd workers from Amazon Mechanical Turk to edit it. Our analysis of these edits shows that, on average, users (i) slightly shortened machine-generated stories, (ii) increased lexical diversity in these stories, and (iii) often replaced nouns and their determiners/articles with pronouns. Our study provides a better understanding on how users receive and edit machine-generated stories,informing future researchers to create more usable and helpful story generation systems.

Connected through hyperedge HE-1 (induced by source paper "visual story post editing"):
  with members:
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "glac net glocal attention cascading networks for multi image cued story generation"
    - "an exploration of neural sequence to sequence architectures for automatic post editing"
    - "attention is all you need"
    - "attention strategies for multi source sequence to sequence learning"
    - "log linear combinations of monolingual and bilingual neural machine translation models for automatic post editing"
    - "deep residual learning for image recognition"

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)