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: "cnn cnn convolutional decoders for image captioning"
  Abstract: Image captioning is a challenging task that combines the field of computer vision and natural language processing. A variety of approaches have been proposed to achieve the goal of automatically describing an image, and recurrent neural network (RNN) or long-short term memory (LSTM) based models dominate this field. However, RNNs or LSTMs cannot be calculated in parallel and ignore the underlying hierarchical structure of a sentence. In this paper, we propose a framework that only employs convolutional neural networks (CNNs) to generate captions. Owing to parallel computing, our basic model is around 3 times faster than NIC (an LSTM-based model) during training time, while also providing better results. We conduct extensive experiments on MSCOCO and investigate the influence of the model width and depth. Compared with LSTM-based models that apply similar attention mechanisms, our proposed models achieves comparable scores of BLEU-1,2,3,4 and METEOR, and higher scores of CIDEr. We also test our model on the paragraph annotation dataset, and get higher CIDEr score compared with hierarchical LSTMs

Connected through hyperedge HE-1 (induced by source paper "deep multimodal image text embeddings for automatic cross media retrieval"):
  with members:
    - "stacked cross attention for image text matching"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "twitter100k a real world dataset for weakly supervised cross media retrieval"
    - "dual path convolutional image text embedding with instance loss"
    - "leveraging visual question answering for image caption ranking"
    - "aggregated residual transformations for deep neural networks"
    - "instance aware image and sentence matching with selective multimodal lstm"
    - "linking image and text with 2 way nets"

Connected through hyperedge HE-2 (induced by source paper "describing like humans on diversity in image captioning"):
  with members:
    - "discriminability objective for training descriptive captions"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "gated hierarchical attention for image captioning"
    - "learning to evaluate image captioning"
    - "improved image captioning via policy gradient optimization of spider"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "convolutional image captioning"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"

Connected through hyperedge HE-3 (induced by source paper "image captioning based on deep learning methods a survey"):
  with members:
    - "learning to guide decoding for image captioning"
    - "rethinking the form of latent states in image captioning"
    - "exploring visual relationship for image captioning"
    - "mat a multimodal attentive translator for image captioning"
    - "convolutional image captioning"
    - "review networks for caption generation"
    - "image captioning with semantic attention"
    - "spice semantic propositional image caption evaluation"

Connected through hyperedge HE-4 (induced by source paper "gated hierarchical attention for image captioning"):
  with members:
    - "dual recurrent attention units for visual question answering"
    - "learning to evaluate image captioning"
    - "learn to pay attention"
    - "improved image captioning via policy gradient optimization of spider"
    - "attention is all you need"
    - "convolutional image captioning"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-5 (induced by source paper "fast diverse and accurate image captioning guided by part of speech"):
  with members:
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "object counts bringing explicit detections back into image captioning"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "improved image captioning via policy gradient optimization of spider"
    - "learning two branch neural networks for image text matching tasks"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "convolutional 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)