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: "language based image editing with recurrent attentive models"
  Abstract: We investigate the problem of Language-Based Image Editing (LBIE) in this work. Given a source image and a natural language description, we want to generate a target image by editing the source im- age based on the description. We propose a generic modeling framework for two sub-tasks of LBIE: language-based image segmentation and image colorization. The framework uses recurrent attentive models to fuse image and language features. Instead of using a fixed step size, we introduce for each re- gion of the image a termination gate to dynamically determine in each inference step whether to continue extrapolating additional information from the textual description. The effectiveness of the framework has been validated on three datasets. First, we introduce a synthetic dataset, called CoSaL, to evaluate the end-to-end performance of our LBIE system. Second, we show that the framework leads to state-of-the- art performance on image segmentation on the ReferIt dataset. Third, we present the first language-based colorization result on the Oxford-102 Flowers dataset, laying the foundation for future research.

Connected through hyperedge HE-1 (induced by source paper "adjusting image attributes of localized regions with low level dialogue"):
  with members:
    - "a multimodal dialogue system for conversational image editing"
    - "expressing visual relationships via language"
    - "learning to globally edit images with textual description"
    - "contextual slot carryover for disparate schemas"
    - "learning to describe differences between pairs of similar images"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "a system for automated image editing from natural language commands"
    - "mattnet modular attention network for referring expression comprehension"

Connected through hyperedge HE-2 (induced by source paper "multi step reasoning via recurrent dual attention for visual dialog"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "reasoning visual dialogs with structural and partial observations"
    - "making history matter history advantage sequence training for visual dialog"
    - "dual attention networks for visual reference resolution in visual dialog"
    - "generative visual dialogue system via adaptive reasoning and weighted likelihood estimation"
    - "factor graph attention"
    - "relation aware graph attention network for visual question answering"
    - "flipdial a generative model for two way visual dialogue"

Connected through hyperedge HE-3 (induced by source paper "a survey and taxonomy of adversarial neural networks for text to image synthesis"):
  with members:
    - "object driven text to image synthesis via adversarial training"
    - "how generative adversarial networks and their variants work an overview"
    - "mirrorgan learning text to image generation by redescription"
    - "dm gan dynamic memory generative adversarial networks for text to image synthesis"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "adversarial learning of semantic relevance in text to image synthesis"
    - "mc gan multi conditional generative adversarial network for image synthesis"

Connected through hyperedge HE-4 (induced by source paper "expressing visual relationships via language"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "learning to globally edit images with textual description"
    - "discriminability objective for training descriptive captions"
    - "learning to describe differences between pairs of similar images"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "learning to color from language"
    - "pragmatically informative image captioning with character level reference"
    - "reinforced video captioning with entailment rewards"

Connected through hyperedge HE-5 (induced by source paper "tag2pix line art colorization using text tag with secat and changing loss"):
  with members:
    - "user guided deep anime line art colorization with conditional adversarial networks"
    - "learning to color from language"
    - "a style based generator architecture for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "semantic image synthesis via adversarial learning"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "comicolorization semi automatic manga colorization"
    - "towards the automatic anime characters creation with generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "bilinear representation for language based image editing using conditional generative adversarial networks"):
  with members:
    - "language guided fashion image manipulation with feature wise transformations"
    - "learning to color from language"
    - "semantic image synthesis via adversarial learning"
    - "be your own prada fashion synthesis with structural coherence"
    - "film visual reasoning with a general conditioning layer"
    - "residual attention network for image classification"
    - "hadamard product for low rank bilinear pooling"
    - "low rank bilinear pooling for fine grained classification"

Connected through hyperedge HE-7 (induced by source paper "learning to globally edit images with textual description"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "chatpainter improving text to image generation using dialogue"
    - "interactive image manipulation with natural language instruction commands"
    - "cross sentence n ary relation extraction with graph lstms"
    - "comprehension guided referring expressions"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "i2t2i learning text to image synthesis with textual data augmentation"
    - "towards diverse and natural image descriptions via a conditional gan"

Connected through hyperedge HE-8 (induced by source paper "composing text and image for image retrieval an empirical odyssey"):
  with members:
    - "attributes as operators"
    - "mattnet modular attention network for referring expression comprehension"
    - "dynamic few shot visual learning without forgetting"
    - "dialog based interactive image retrieval"
    - "in defense of the triplet loss for person re identification"
    - "a simple neural network module for relational reasoning"
    - "prototypical networks for few shot learning"
    - "ivqa inverse visual question answering"

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)