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: "photographic text to image synthesis with a hierarchically nested adversarial network"
  Abstract: This paper presents a novel method to deal with the challenging task of generating photographic images conditioned on semantic image descriptions. Our method introduces accompanying hierarchical-nested adversarial objectives inside the network hierarchies, which regularize mid-level representations and assist generator training to capture the complex image statistics. We present an extensile single-stream generator architecture to better adapt the jointed discriminators and push generated images up to high resolutions. We adopt a multi-purpose adversarial loss to encourage more effective image and text information usage in order to improve the semantic consistency and image fidelity simultaneously. Furthermore, we introduce a new visual-semantic similarity measure to evaluate the semantic consistency of generated images. With extensive experimental validation on three public datasets, our method significantly improves previous state of the arts on all datasets over different evaluation metrics.

Connected through hyperedge HE-1 (induced by source paper "adversarial code learning for image generation"):
  with members:
    - "image transformer"
    - "semi supervised learning with gans revisiting manifold regularization"
    - "large scale gan training for high fidelity natural image synthesis"
    - "dist gan an improved gan using distance constraints"
    - "mcgan mean and covariance feature matching gan"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "latent normalizing flows for many to many cross domain mappings"):
  with members:
    - "sequential latent spaces for modeling the intention during diverse image captioning"
    - "preventing posterior collapse with delta vaes"
    - "mirrorgan learning text to image generation by redescription"
    - "conditional flow variational autoencoders for structured sequence prediction"
    - "m3d gan multi modal multi domain translation with universal attention"
    - "semantics disentangling for text to image generation"
    - "describing like humans on diversity in image captioning"
    - "analyzing inverse problems with invertible neural networks"

Connected through hyperedge HE-3 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling"):
  with members:
    - "object driven text to image synthesis via adversarial training"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "text adaptive generative adversarial networks manipulating images with natural language"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "recent advances in zero shot recognition"
    - "self supervised learning of visual features through embedding images into text topic spaces"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"

Connected through hyperedge HE-4 (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"
    - "adversarial learning of semantic relevance in text to image synthesis"
    - "mc gan multi conditional generative adversarial network for image synthesis"
    - "fast converging conditional generative adversarial networks for image synthesis"

Connected through hyperedge HE-5 (induced by source paper "on the evaluation of conditional gans"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "classification accuracy score for conditional generative models"
    - "diversity sensitive conditional generative adversarial networks"
    - "from variational to deterministic autoencoders"
    - "hype a benchmark for human eye perceptual evaluation of generative models"
    - "semantic image synthesis with spatially adaptive normalization"

Connected through hyperedge HE-6 (induced by source paper "facial synthesis from visual attributes via sketch using multi scale generators"):
  with members:
    - "polarimetric thermal to visible face verification via attribute preserved synthesis"
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "face synthesis from visual attributes via sketch using conditional vaes and gans"
    - "on hallucinating context and background pixels from a face mask using multi scale gans"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "modulating early visual processing by language"
    - "decomposing motion and content for natural video sequence prediction"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"

Connected through hyperedge HE-7 (induced by source paper "semantic object accuracy for generative text to image synthesis"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "interactive image generation using scene graphs"
    - "realistic image generation using region phrase attention"
    - "object driven text to image synthesis via adversarial training"
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "image synthesis from reconfigurable layout and style"
    - "visual relation conscious image generation from structured text"
    - "classification accuracy score for conditional generative models"

Connected through hyperedge HE-8 (induced by source paper "controllable and progressive image extrapolation"):
  with members:
    - "visual dynamics stochastic future generation via layered cross convolutional networks"
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "foreground aware image inpainting"
    - "semantic image synthesis with spatially adaptive normalization"
    - "very long natural scenery image prediction by outpainting"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "non stationary texture synthesis by adversarial expansion"
    - "linknet relational embedding for scene graph"

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)