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: "mode seeking generative adversarial networks for diverse image synthesis"
  Abstract: Most conditional generation tasks expect diverse outputs given a single conditional context. However, conditional generative adversarial networks (cGANs) often focus on the prior conditional information and ignore the input noise vectors, which contribute to the output variations. Recent attempts to resolve the mode collapse issue for cGANs are usually task-specific and computationally expensive. In this work, we propose a simple yet effective regularization term to address the mode collapse issue for cGANs. The proposed method explicitly maximizes the ratio of the distance between generated images with respect to the corresponding latent codes, thus encouraging the generators to explore more minor modes during training. This mode seeking regularization term is readily applicable to various conditional generation tasks without imposing training overhead or modifying the original network structures. We validate the proposed algorithm on three conditional image synthesis tasks including categorical generation, image-to-image translation, and text-to-image synthesis with different baseline models. Both qualitative and quantitative results demonstrate the effectiveness of the proposed regularization method for improving diversity without loss of quality.

Connected through hyperedge HE-1 (induced by source paper "when relation networks meet gans relation gans with triplet loss"):
  with members:
    - "improving generalization and stability of generative adversarial networks"
    - "gan qp a novel gan framework without gradient vanishing and lipschitz constraint"
    - "the relativistic discriminator a key element missing from standard gan"
    - "deep video generation prediction and completion of human action sequences"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on convergence and stability of gans"
    - "which training methods for gans do actually converge"

Connected through hyperedge HE-2 (induced by source paper "semantic pyramid for image generation"):
  with members:
    - "seeing what a gan cannot generate"
    - "on the steerability of generative adversarial networks"
    - "image resizing by reconstruction from deep features"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-3 (induced by source paper "one to one mapping for unpaired image to image translation"):
  with members:
    - "towards learning a self inverse network for bidirectional image to image translation"
    - "towards instance level image to image translation"
    - "task driven generative modeling for unsupervised domain adaptation application to x ray image segmentation"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "analyzing inverse problems with invertible neural networks"
    - "recycle gan unsupervised video retargeting"
    - "multimodal unsupervised image to image translation"
    - "attention gan for object transfiguration in wild images"

Connected through hyperedge HE-4 (induced by source paper "hierarchical modes exploring in generative adversarial networks"):
  with members:
    - "on gans and gmms"
    - "stochastic adversarial video prediction"
    - "on line adaptative curriculum learning for gans"
    - "visual data synthesis via gan for zero shot video classification"
    - "jointgan multi domain joint distribution learning with generative adversarial nets"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "semi supervised fusedgan for conditional image generation"
    - "drit diverse image to image translation via disentangled representations"

Connected through hyperedge HE-5 (induced by source paper "on the evaluation of conditional gans"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "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"
    - "cgans with projection discriminator"

Connected through hyperedge HE-6 (induced by source paper "stargan v2 diverse image synthesis for multiple domains"):
  with members:
    - "coloring with limited data few shot colorization via memory augmented networks"
    - "tag2pix line art colorization using text tag with secat and changing loss"
    - "multi mapping image to image translation via learning disentanglement"
    - "large scale adversarial representation learning"
    - "few shot unsupervised image to image translation"
    - "sdit scalable and diverse cross domain image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "high fidelity image generation with fewer labels"

Connected through hyperedge HE-7 (induced by source paper "multimodal image outpainting with regularized normalized diversification"):
  with members:
    - "object driven text to image synthesis via adversarial training"
    - "normalized diversification"
    - "putting humans in a scene learning affordance in 3d indoor environments"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "binge watching scaling affordance learning from sitcoms"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"

Connected through hyperedge HE-8 (induced by source paper "gmm unit unsupervised multi domain and multi modal image to image translation via attribute gaussian mixture modelling"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "transgaga geometry aware unsupervised image to image translation"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "ganimation anatomically aware facial animation from a single image"

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)