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: "hierarchically structured reinforcement learning for topically coherent visual story generation"
  Abstract: We propose a hierarchically structured reinforcement learning approach to address the challenges of planning for generating coherent multi-sentence stories for the visual storytelling task. Within our framework, the task of generating a story given a sequence of images is divided across a two-level hierarchical decoder. The high-level decoder constructs a plan by generating a semantic concept (i.e., topic) for each image in sequence. The low-level decoder generates a sentence for each image using a semantic compositional network, which effectively grounds the sentence generation conditioned on the topic. The two decoders are jointly trained end-to-end using reinforcement learning. We evaluate our model on the visual storytelling (VIST) dataset. Empirical results from both automatic and human evaluations demonstrate that the proposed hierarchically structured reinforced training achieves significantly better performance compared to a strong flat deep reinforcement learning baseline.

Connected through hyperedge HE-1 (induced by source paper "hide and tell learning to bridge photo streams for visual storytelling"):
  with members:
    - "hierarchical photo scene encoder for album storytelling"
    - "linknet relational embedding for scene graph"
    - "attention is all you need"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "learning by asking questions"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "hierarchical recurrent neural encoder for video representation with application to captioning"

Connected through hyperedge HE-2 (induced by source paper "visual storytelling via predicting anchor word embeddings in the stories"):
  with members:
    - "contextualize show and tell a neural visual storyteller"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "deep residual learning for image recognition"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "show and tell a neural image caption generator"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "improving sequence to sequence learning via optimal transport"):
  with members:
    - "sequence generation with guider network"
    - "policy optimization as wasserstein gradient flows"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "improving gans using optimal transport"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "improved image captioning via policy gradient optimization of spider"

Connected through hyperedge HE-4 (induced by source paper "what makes a good story designing composite rewards for visual storytelling"):
  with members:
    - "uniter learning universal image text representations"
    - "learning end to end goal oriented dialog with multiple answers"
    - "plan and write towards better automatic storytelling"
    - "hybrid retrieval generation reinforced agent for medical image report generation"
    - "learning neural templates for text generation"
    - "towards diverse text generation with inverse reinforcement learning"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "hierarchical neural story generation"

Connected through hyperedge HE-5 (induced by source paper "tiger text to image grounding for image caption evaluation"):
  with members:
    - "stacked cross attention for image text matching"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "neural approaches to conversational ai"
    - "learning to evaluate image captioning"
    - "learning two branch neural networks for image text matching tasks"
    - "attacking visual language grounding with adversarial examples a case study on neural image captioning"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "towards diverse and natural image descriptions via a conditional gan"

Connected through hyperedge HE-6 (induced by source paper "keep it consistent topic aware storytelling from an image stream via iterative multi agent communication"):
  with members:
    - "topic aware neural keyphrase generation for social media language"
    - "hierarchical photo scene encoder for album storytelling"
    - "on the automatic generation of medical imaging reports"
    - "a study of reinforcement learning for neural machine translation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "towards diverse and natural image descriptions via a conditional gan"

Connected through hyperedge HE-7 (induced by source paper "writerforcing generating more interesting story endings"):
  with members:
    - "story ending generation with incremental encoding and commonsense knowledge"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "generating more interesting responses in neural conversation models with distributional constraints"
    - "incorporating structured commonsense knowledge in story completion"
    - "toward controlled generation of text"
    - "emotional chatting machine emotional conversation generation with internal and external memory"

Connected through hyperedge HE-8 (induced by source paper "informative visual storytelling with cross modal rules"):
  with members:
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "glac net glocal attention cascading networks for multi image cued story generation"
    - "using inter sentence diverse beam search to reduce redundancy in visual storytelling"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "image captioning with semantic attention"
    - "rare association rule mining for network intrusion detection"
    - "boosting image captioning with attributes"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"

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.CV(Computer Vision and Pattern Recognition)