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: "guiding long short term memory for image caption generation"
  Abstract: In this work we focus on the problem of image caption generation. We propose an extension of the long short term memory (LSTM) model, which we coin gLSTM for short. In particular, we add semantic information extracted from the image as extra input to each unit of the LSTM block, with the aim of guiding the model towards solutions that are more tightly coupled to the image content. Additionally, we explore different length normalization strategies for beam search in order to prevent from favoring short sentences. On various benchmark datasets such as Flickr8K, Flickr30K and MS COCO, we obtain results that are on par with or even outperform the current state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "hierarchical memory decoding for video captioning"):
  with members:
    - "less is more picking informative frames for video captioning"
    - "middle out decoding"
    - "reconstruction network for video captioning"
    - "attention is all you need"
    - "hierarchical lstm with adjusted temporal attention for video captioning"
    - "convolutional image captioning"
    - "consensus based sequence training for video captioning"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "unpaired cross lingual image caption generation with self supervised rewards"):
  with members:
    - "unsupervised bilingual lexicon induction from mono lingual multimodal data"
    - "good news everyone context driven entity aware captioning for news images"
    - "from words to sentences a progressive learning approach for zero resource machine translation with visual pivots"
    - "discriminability objective for training descriptive captions"
    - "context aware visual policy network for sequence level image captioning"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "unsupervised image captioning"
    - "unpaired image captioning by language pivoting"

Connected through hyperedge HE-3 (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-4 (induced by source paper "multimodal semantic attention network for video captioning"):
  with members:
    - "video captioning with transferred semantic attributes"
    - "semantic compositional networks for visual captioning"
    - "image captioning with semantic attention"
    - "jointly modeling embedding and translation to bridge video and language"
    - "what value do explicit high level concepts have in vision to language problems"
    - "delving deeper into convolutional networks for learning video representations"
    - "hierarchical recurrent neural encoder for video representation with application to captioning"
    - "sequence to sequence video to text"

Connected through hyperedge HE-5 (induced by source paper "unpaired image captioning by language pivoting"):
  with members:
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "zero resource neural machine translation with multi agent communication game"
    - "texygen a benchmarking platform for text generation models"
    - "improved image captioning via policy gradient optimization of spider"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "ai challenger a large scale dataset for going deeper in image understanding"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"

Connected through hyperedge HE-6 (induced by source paper "image captioning based on a hierarchical attention mechanism and policy gradient optimization"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "adversarial neural machine translation"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "image captioning with semantic attention"
    - "nips 2016 tutorial generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "a sequential guiding network with attention for image captioning"):
  with members:
    - "learning to guide decoding for image captioning"
    - "improved image captioning via policy gradient optimization of spider"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "mat a multimodal attentive translator for image captioning"
    - "spice semantic propositional image caption evaluation"
    - "automatic description generation from images a survey of models datasets and evaluation measures"
    - "boosting image captioning with attributes"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"

Connected through hyperedge HE-8 (induced by source paper "learning to guide decoding for image captioning"):
  with members:
    - "review networks for caption generation"
    - "image captioning with semantic attention"
    - "text guided attention model for image captioning"
    - "boosting image captioning with attributes"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "an empirical study of language cnn for image captioning"
    - "recurrent image captioner describing images with spatial invariant transformation and attention filtering"
    - "self critical sequence training 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)