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 attentive rnn for album summarization and storytelling"
  Abstract: We address the problem of end-to-end visual storytelling. Given a photo album, our model first selects the most representative (summary) photos, and then composes a natural language story for the album. For this task, we make use of the Visual Storytelling dataset and a model composed of three hierarchically-attentive Recurrent Neural Nets (RNNs) to: encode the album photos, select representative (summary) photos, and compose the story. Automatic and human evaluations show our model achieves better performance on selection, generation, and retrieval than baselines.

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"
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "attention is all you need"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "learning by asking questions"
    - "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"
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "no metrics are perfect adversarial reward learning for visual 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 "hierarchical photo scene encoder for album storytelling"):
  with members:
    - "learning to guide decoding for image captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "hierarchical boundary aware neural encoder for video captioning"

Connected through hyperedge HE-4 (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"
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "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"
    - "towards diverse and natural image descriptions via a conditional gan"

Connected through hyperedge HE-5 (induced by source paper "informative visual storytelling with cross modal rules"):
  with members:
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "visual understanding and narration a deeper understanding and explanation of visual scenes"):
  with members:
    - "from recognition to cognition visual commonsense reasoning"
    - "a pipeline for creative visual storytelling"
    - "temporal event knowledge acquisition via identifying narratives"
    - "scoutbot a dialogue system for collaborative navigation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "dock detecting objects by transferring common sense knowledge"
    - "hierarchical neural story generation"
    - "the something something video database for learning and evaluating visual common sense"

Connected through hyperedge HE-7 (induced by source paper "incorporating textual evidence in visual storytelling"):
  with members:
    - "using deep object features for image descriptions"
    - "contextualize show and tell a neural visual storyteller"
    - "learning to guide decoding for image captioning"
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "simnet stepwise image topic merging network for generating detailed and comprehensive image captions"
    - "object counts bringing explicit detections back into image captioning"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "glac net glocal attention cascading networks for multi image cued story generation"

Connected through hyperedge HE-8 (induced by source paper "the steep road to happily ever after an analysis of current visual storytelling models"):
  with members:
    - "contextualize show and tell a neural visual storyteller"
    - "a pipeline for creative visual storytelling"
    - "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"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "story generation from sequence of independent short descriptions"
    - "sort story sorting jumbled images and captions into stories"

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)