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: "learning a recurrent visual representation for image caption generation"
  Abstract: In this paper we explore the bi-directional mapping between images and their sentence-based descriptions. We propose learning this mapping using a recurrent neural network. Unlike previous approaches that map both sentences and images to a common embedding, we enable the generation of novel sentences given an image. Using the same model, we can also reconstruct the visual features associated with an image given its visual description. We use a novel recurrent visual memory that automatically learns to remember long-term visual concepts to aid in both sentence generation and visual feature reconstruction. We evaluate our approach on several tasks. These include sentence generation, sentence retrieval and image retrieval. State-of-the-art results are shown for the task of generating novel image descriptions. When compared to human generated captions, our automatically generated captions are preferred by humans over $19.8\%$ of the time. Results are better than or comparable to state-of-the-art results on the image and sentence retrieval tasks for methods using similar visual features.

Connected through hyperedge HE-1 (induced by source paper "on architectures for including visual information in neural language models for image description"):
  with members:
    - "transfer learning from language models to image caption generators better models may not transfer better"
    - "visual entailment a novel task for fine grained image understanding"
    - "do better imagenet models transfer better"
    - "quantifying the amount of visual information used by neural caption generators"
    - "pre gen metrics predicting caption quality metrics without generating captions"
    - "object counts bringing explicit detections back into image captioning"
    - "the memad submission to the wmt18 multimodal translation task"
    - "grounded textual entailment"

Connected through hyperedge HE-2 (induced by source paper "a survey of multi view representation learning"):
  with members:
    - "word2visualvec cross media retrieval by visual feature prediction"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"
    - "globally convergent stochastic optimization for canonical correlation analysis"
    - "word2visualvec image and video to sentence matching by visual feature prediction"
    - "convolutional two stream network fusion for video action recognition"
    - "deep cross modal hashing"

Connected through hyperedge HE-3 (induced by source paper "lucss language based user customized colourization of scene sketches"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "sketchyscene richly annotated scene sketches"
    - "texturegan controlling deep image synthesis with texture patches"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "outline colorization through tandem adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "hierarchical lstms with adaptive attention for visual captioning"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "video captioning with transferred semantic attributes"
    - "semantic compositional networks for visual captioning"
    - "image captioning with semantic attention"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "video captioning with multi faceted attention"

Connected through hyperedge HE-5 (induced by source paper "where to put the image in an image caption generator"):
  with members:
    - "image captioning with semantic attention"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "automatic description generation from images a survey of models datasets and evaluation measures"
    - "boosting image captioning with attributes"
    - "a hierarchical approach for generating descriptive image paragraphs"
    - "image caption generation with text conditional semantic attention"
    - "theano a python framework for fast computation of mathematical expressions"
    - "language models for image captioning the quirks and what works"

Connected through hyperedge HE-6 (induced by source paper "multi modal gated recurrent units for image description"):
  with members:
    - "lstm a search space odyssey"
    - "actor critic sequence training for image captioning"
    - "mat a multimodal attentive translator for image captioning"
    - "remote sensing image scene classification benchmark and state of the art"
    - "what value do explicit high level concepts have in vision to language problems"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "skip thought vectors"
    - "a c library for multimodal deep learning"

Connected through hyperedge HE-7 (induced by source paper "semantically invariant text to image generation"):
  with members:
    - "semantic sentence embeddings for paraphrasing and text summarization"
    - "in defense of the triplet loss for person re identification"
    - "starspace embed all the things"
    - "progressive growing of gans for improved quality stability and variation"
    - "recurrent topic transition gan for visual paragraph generation"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "end to end learning of deep visual representations for image retrieval"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"

Connected through hyperedge HE-8 (induced by source paper "deepsic deep semantic image compression"):
  with members:
    - "full resolution image compression with recurrent neural networks"
    - "end to end optimized image compression"
    - "yolo9000 better faster stronger"
    - "generating images with recurrent adversarial networks"
    - "densely connected convolutional networks"
    - "hierarchical question image co attention for visual question answering"
    - "scene recognition with cnns objects scales and dataset bias"
    - "visual7w grounded question answering in images"

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)