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: "tac gan text conditioned auxiliary classifier generative adversarial network"
  Abstract: In this work, we present the Text Conditioned Auxiliary Classifier Generative Adversarial Network, (TAC-GAN) a text to image Generative Adversarial Network (GAN) for synthesizing images from their text descriptions. Former approaches have tried to condition the generative process on the textual data; but allying it to the usage of class information, known to diversify the generated samples and improve their structural coherence, has not been explored. We trained the presented TAC-GAN model on the Oxford-102 dataset of flowers, and evaluated the discriminability of the generated images with Inception-Score, as well as their diversity using the Multi-Scale Structural Similarity Index (MS-SSIM). Our approach outperforms the state-of-the-art models, i.e., its inception score is 3.45, corresponding to a relative increase of 7.8% compared to the recently introduced StackGan. A comparison of the mean MS-SSIM scores of the training and generated samples per class shows that our approach is able to generate highly diverse images with an average MS-SSIM of 0.14 over all generated classes.

Connected through hyperedge HE-1 (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"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"

Connected through hyperedge HE-2 (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-3 (induced by source paper "generative modeling for small data object detection"):
  with members:
    - "inserting videos into videos"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "versatile auxiliary classifier with generative adversarial network vac gan multi class scenarios"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "context aware synthesis and placement of object instances"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "learning hierarchical semantic image manipulation through structured representations"

Connected through hyperedge HE-4 (induced by source paper "dual adversarial inference for text to image synthesis"):
  with members:
    - "mirrorgan learning text to image generation by redescription"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "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"
    - "image to image translation for cross domain disentanglement"
    - "drit diverse image to image translation via disentangled representations"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-5 (induced by source paper "dm gan dynamic memory generative adversarial networks for text to image synthesis"):
  with members:
    - "sim real joint reinforcement transfer for 3d indoor navigation"
    - "adversarial learning with local coordinate coding"
    - "spectral normalization for generative adversarial networks"
    - "text to image synthesis via symmetrical distillation networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "semantic image synthesis via adversarial learning"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "classical music generation in distinct dastgahs with alimnet acgan"):
  with members:
    - "instrument independent dastgah recognition of iranian classical music using azarnet"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "a report on sound event detection with different binaural features"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "svsgan singing voice separation via generative adversarial network"
    - "neural discrete representation learning"
    - "samplernn an unconditional end to end neural audio generation model"

Connected through hyperedge HE-7 (induced by source paper "memefacegenerator adversarial synthesis of chinese meme face from natural sentences"):
  with members:
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "conditional generative adversarial networks for emoji synthesis with word embedding manipulation"
    - "learning what and where to draw"
    - "generative adversarial text to image synthesis"
    - "vqa visual question answering"
    - "stacked attention networks for image question answering"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-8 (induced by source paper "photographic text to image synthesis with a hierarchically nested adversarial network"):
  with members:
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "tell me where to look guided attention inference network"
    - "context encoding for semantic segmentation"
    - "dual discriminator generative adversarial nets"
    - "began boundary equilibrium generative adversarial networks"
    - "towards adversarial retinal image synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "semantic image synthesis via adversarial learning"

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)