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: "simnet stepwise image topic merging network for generating detailed and comprehensive image captions"
  Abstract: The encode-decoder framework has shown recent success in image captioning. Visual attention, which is good at detailedness, and semantic attention, which is good at comprehensiveness, have been separately proposed to ground the caption on the image. In this paper, we propose the Stepwise Image-Topic Merging Network (simNet) that makes use of the two kinds of attention at the same time. At each time step when generating the caption, the decoder adaptively merges the attentive information in the extracted topics and the image according to the generated context, so that the visual information and the semantic information can be effectively combined. The proposed approach is evaluated on two benchmark datasets and reaches the state-of-the-art performances.(The code is available at this https URL)

Connected through hyperedge HE-1 (induced by source paper "multimodal matching transformer for live commenting"):
  with members:
    - "multi step reasoning via recurrent dual attention for visual dialog"
    - "video description a survey of methods datasets and evaluation metrics"
    - "livebot generating live video comments based on visual and textual contexts"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "video captioning via hierarchical reinforcement learning"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "see hear and read deep aligned representations"

Connected through hyperedge HE-2 (induced by source paper "explicit sparse transformer concentrated attention through explicit selection"):
  with members:
    - "adaptively sparse transformers"
    - "muse parallel multi scale attention for sequence to sequence learning"
    - "pay less attention with lightweight and dynamic convolutions"
    - "sparse sequence to sequence models"
    - "joint source target self attention with locality constraints"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fixup initialization residual learning without normalization"
    - "universal transformers"

Connected through hyperedge HE-3 (induced by source paper "sparse and structured visual attention"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "sparse sequence to sequence models"
    - "deep modular co attention networks for visual question answering"
    - "bilinear attention networks"
    - "neural baby talk"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "sparse and constrained attention for neural machine translation"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-4 (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"
    - "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"
    - "stories for images in sequence by using visual and narrative components"

Connected through hyperedge HE-5 (induced by source paper "aligning visual regions and textual concepts learning fine grained image representations for image captioning"):
  with members:
    - "exploring and distilling cross modal information for image captioning"
    - "livebot generating live video comments based on visual and textual contexts"
    - "neural baby talk"
    - "recurrent fusion network for image captioning"
    - "exploring visual relationship for image captioning"
    - "image captioning with semantic attention"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "boosting image captioning with attributes"

Connected through hyperedge HE-6 (induced by source paper "livebot generating live video comments based on visual and textual contexts"):
  with members:
    - "query and output generating words by querying distributed word representations for paraphrase generation"
    - "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"
    - "attention is all you need"
    - "weakly supervised dense video captioning"
    - "multi task video captioning with video and entailment generation"
    - "analyzing the behavior of visual question answering models"
    - "frame and segment level features and candidate pool evaluation for video caption generation"
    - "vqa visual question answering"

Connected through hyperedge HE-7 (induced by source paper "exploration on grounded word embedding matching words and images with image enhanced skip gram model"):
  with members:
    - "zero resource neural machine translation with multi agent communication game"
    - "regularizing output distribution of abstractive chinese social media text summarization for improved semantic consistency"
    - "label embedding network learning label representation for soft training of deep networks"
    - "query and output generating words by querying distributed word representations for paraphrase generation"
    - "image pivoting for learning multilingual multimodal representations"
    - "exploring models and data for image question answering"
    - "learning deep structure preserving image text embeddings"
    - "multimodal convolutional neural networks for matching image and sentence"

Connected through hyperedge HE-8 (induced by source paper "live video comment generation based on surrounding frames and live comments"):
  with members:
    - "bag of words as target for neural machine translation"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "weakly supervised dense video captioning"
    - "multi task video captioning with video and entailment generation"
    - "show attend and tell neural image caption generation with visual attention"
    - "sequence to sequence video to text"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

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)