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: "simple image description generator via a linear phrase based approach"
  Abstract: Generating a novel textual description of an image is an interesting problem that connects computer vision and natural language processing. In this paper, we present a simple model that is able to generate descriptive sentences given a sample image. This model has a strong focus on the syntax of the descriptions. We train a purely bilinear model that learns a metric between an image representation (generated from a previously trained Convolutional Neural Network) and phrases that are used to described them. The system is then able to infer phrases from a given image sample. Based on caption syntax statistics, we propose a simple language model that can produce relevant descriptions for a given test image using the phrases inferred. Our approach, which is considerably simpler than state-of-the-art models, achieves comparable results on the recently release Microsoft COCO dataset.

Connected through hyperedge HE-1 (induced by source paper "figure captioning with reasoning and sequence level training"):
  with members:
    - "dvqa understanding data visualizations via question answering"
    - "visual question answering datasets algorithms and future challenges"
    - "a simple neural network module for relational reasoning"
    - "figureqa an annotated figure dataset for visual reasoning"
    - "a focused dynamic attention model for visual question answering"
    - "dualnet domain invariant network for visual question answering"
    - "multimodal residual learning for visual qa"
    - "an actor critic algorithm for sequence prediction"

Connected through hyperedge HE-2 (induced by source paper "factual or emotional stylized image captioning with adaptive learning and attention"):
  with members:
    - "vizwiz grand challenge answering visual questions from blind people"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "perceptual losses for real time style transfer and super resolution"
    - "image captioning with semantic attention"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-3 (induced by source paper "deep reinforcement learning based image captioning with embedding reward"):
  with members:
    - "image captioning with semantic attention"
    - "face detection with the faster r cnn"
    - "ssd single shot multibox detector"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "diverse beam search decoding diverse solutions from neural sequence models"
    - "asynchronous methods for deep reinforcement learning"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "dense captioning with joint inference and visual context"

Connected through hyperedge HE-4 (induced by source paper "towards diverse and natural image descriptions via a conditional gan"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "detecting visual relationships with deep relational networks"
    - "scene graph generation from objects phrases and region captions"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "review networks for caption generation"
    - "image captioning with semantic attention"
    - "spice semantic propositional image caption evaluation"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-5 (induced by source paper "image captioning with semantic attention"):
  with members:
    - "what value do explicit high level concepts have in vision to language problems"
    - "show attend and tell neural image caption generation with visual attention"
    - "exploring nearest neighbor approaches for image captioning"
    - "learning like a child fast novel visual concept learning from sentence descriptions of images"
    - "microsoft coco captions data collection and evaluation server"
    - "draw a recurrent neural network for image generation"
    - "going deeper with convolutions"
    - "deep captioning with multimodal recurrent neural networks m rnn"

Connected through hyperedge HE-6 (induced by source paper "learning like a child fast novel visual concept learning from sentence descriptions of images"):
  with members:
    - "multimodal convolutional neural networks for matching image and sentence"
    - "vqa visual question answering"
    - "show attend and tell neural image caption generation with visual attention"
    - "exploring nearest neighbor approaches for image captioning"
    - "microsoft coco captions data collection and evaluation server"
    - "are you talking to a machine dataset and methods for multilingual image question answering"
    - "very deep convolutional networks for large scale image recognition"
    - "unifying visual semantic embeddings with multimodal neural language models"

Connected through hyperedge HE-7 (induced by source paper "are you talking to a machine dataset and methods for multilingual image question answering"):
  with members:
    - "exploring models and data for image question answering"
    - "vqa visual question answering"
    - "show attend and tell neural image caption generation with visual attention"
    - "learning like a child fast novel visual concept learning from sentence descriptions of images"
    - "ask your neurons a neural based approach to answering questions about images"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "unifying visual semantic embeddings with multimodal neural language models"

Connected through hyperedge HE-8 (induced by source paper "exploring nearest neighbor approaches for image captioning"):
  with members:
    - "language models for image captioning the quirks and what works"
    - "phrase based image captioning"
    - "combining language and vision with a multimodal skip gram model"
    - "very deep convolutional networks for large scale image recognition"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "deep captioning with multimodal recurrent neural networks m rnn"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "cider consensus based image description evaluation"

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)