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: "imagespirit verbal guided image parsing"
  Abstract: Humans describe images in terms of nouns and adjectives while algorithms operate on images represented as sets of pixels. Bridging this gap between how humans would like to access images versus their typical representation is the goal of image parsing, which involves assigning object and attribute labels to pixels. In this article we propose treating nouns as object labels and adjectives as visual attribute labels. This allows us to formulate the image parsing problem as one of jointly estimating per-pixel object and attribute labels from a set of training images. We propose an efficient (interactive time) solution. Using the extracted labels as handles, our system empowers a user to verbally refine the results. This enables hands-free parsing of an image into pixel-wise object/attribute labels that correspond to human semantics. Verbally selecting objects of interest enables a novel and natural interaction modality that can possibly be used to interact with new generation devices (e.g., smartphones, Google Glass, livingroom devices). We demonstrate our system on a large number of real-world images with varying complexity. To help understand the trade-offs compared to traditional mouse-based interactions, results are reported for both a large-scale quantitative evaluation and a user study.

Connected through hyperedge HE-1 (induced by source paper "semantic hierarchy emerges in the deep generative representations for scene synthesis"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "singan learning a generative model from a single natural image"
    - "edge semantic learning strategy for layout estimation in indoor environment"
    - "ganalyze toward visual definitions of cognitive image properties"
    - "semantic image synthesis with spatially adaptive normalization"
    - "interpreting the latent space of gans for semantic face editing"
    - "3d aware scene manipulation via inverse graphics"
    - "unified perceptual parsing for scene understanding"

Connected through hyperedge HE-2 (induced by source paper "knowledge guided pairwise reconstruction network for weakly supervised referring expression grounding"):
  with members:
    - "referring relationships"
    - "real time referring expression comprehension by single stage grounding network"
    - "mattnet modular attention network for referring expression comprehension"
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "query guided regression network with context policy for phrase grounding"
    - "grounding referring expressions in images by variational context"
    - "comprehension guided referring expressions"
    - "weakly supervised visual grounding of phrases with linguistic structures"

Connected through hyperedge HE-3 (induced by source paper "controllable text to image generation"):
  with members:
    - "text adaptive generative adversarial networks manipulating images with natural language"
    - "semantic image synthesis via adversarial learning"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "neural photo editing with introspective adversarial networks"
    - "learning what and where to draw"

Connected through hyperedge HE-4 (induced by source paper "adaptive reconstruction network for weakly supervised referring expression grounding"):
  with members:
    - "knowledge guided pairwise reconstruction network for weakly supervised referring expression grounding"
    - "real time referring expression comprehension by single stage grounding network"
    - "mattnet modular attention network for referring expression comprehension"
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "query guided regression network with context policy for phrase grounding"
    - "grounding referring expressions in images by variational context"
    - "iqa visual question answering in interactive environments"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"

Connected through hyperedge HE-5 (induced by source paper "manipulating attributes of natural scenes via hallucination"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "a closed form solution to photorealistic image stylization"
    - "multimodal unsupervised image to image translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "semi parametric image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-6 (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"
    - "language based image editing with recurrent attentive models"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "exploiting test time evidence to improve predictions of deep neural networks"):
  with members:
    - "imposing hard constraints on deep networks promises and limitations"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "recurrent multimodal interaction for referring image segmentation"
    - "an overview of multi task learning in deep neural networks"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"
    - "optical flow estimation using a spatial pyramid network"

Connected through hyperedge HE-8 (induced by source paper "a novel technique for evidence based conditional inference in deep neural networks via latent feature perturbation"):
  with members:
    - "generalizing across domains via cross gradient training"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "imposing hard constraints on deep networks promises and limitations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "priming neural networks"
    - "toward multimodal image to image translation"
    - "a semantic loss function for deep learning with symbolic knowledge"

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.GR(Graphics)