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: "gagan geometry aware generative adverserial networks"
  Abstract: Deep generative models learned through adversarial training have become increasingly popular for their ability to generate naturalistic image textures. However, aside from their texture, the visual appearance of objects is significantly influenced by their shape geometry; information which is not taken into account by existing generative models. This paper introduces the Geometry-Aware Generative Adversarial Networks (GAGAN) for incorporating geometric information into the image generation process. Specifically, in GAGAN the generator samples latent variables from the probability space of a statistical shape model. By mapping the output of the generator to a canonical coordinate frame through a differentiable geometric transformation, we enforce the geometry of the objects and add an implicit connection from the prior to the generated object. Experimental results on face generation indicate that the GAGAN can generate realistic images of faces with arbitrary facial attributes such as facial expression, pose, and morphology, that are of better quality than current GAN-based methods. Our method can be used to augment any existing GAN architecture and improve the quality of the images generated.

Connected through hyperedge HE-1 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "a layer based sequential framework for scene generation with gans"
    - "transformable bottleneck networks"
    - "generating multiple objects at spatially distinct locations"
    - "neural voxel renderer learning an accurate and controllable rendering tool"
    - "emergence of object segmentation in perturbed generative models"
    - "escaping plato s cave 3d shape from adversarial rendering"

Connected through hyperedge HE-2 (induced by source paper "hologan unsupervised learning of 3d representations from natural images"):
  with members:
    - "representation learning by rotating your faces"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "visual object networks image generation with disentangled 3d representation"
    - "learning category specific mesh reconstruction from image collections"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "spectral normalization for generative adversarial networks"
    - "towards open set identity preserving face synthesis"

Connected through hyperedge HE-3 (induced by source paper "image disentanglement and uncooperative re entanglement for high fidelity image to image translation"):
  with members:
    - "a two step disentanglement method"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "deep appearance models for face rendering"
    - "mine mutual information neural estimation"
    - "geometry consistent generative adversarial networks for one sided unsupervised domain mapping"
    - "image to image translation for cross domain disentanglement"

Connected through hyperedge HE-4 (induced by source paper "disentangling content and style via unsupervised geometry distillation"):
  with members:
    - "unsupervised discovery of object landmarks as structural representations"
    - "a variational u net for conditional appearance and shape generation"
    - "image to image translation for cross domain disentanglement"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "end to end weakly supervised semantic alignment"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "pose guided person image generation"
    - "neural face editing with intrinsic image disentangling"

Connected through hyperedge HE-5 (induced by source paper "cross view image synthesis using geometry guided conditional gans"):
  with members:
    - "bridging the domain gap for ground to aerial image matching"
    - "cross view image synthesis using conditional gans"
    - "pros and cons of gan evaluation measures"
    - "dual discriminator generative adversarial nets"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "geometry guided adversarial facial expression synthesis"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "deformable generator network unsupervised disentanglement of appearance and geometry"):
  with members:
    - "infogan cr disentangling generative adversarial networks with contrastive regularizers"
    - "motion based generator model unsupervised disentanglement of appearance trackable and intrackable motions in dynamic patterns"
    - "on the fairness of disentangled representations"
    - "flexibly fair representation learning by disentanglement"
    - "multimodal unsupervised image to image translation"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "isolating sources of disentanglement in variational autoencoders"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-7 (induced by source paper "triple consistency loss for pairing distributions in gan based face synthesis"):
  with members:
    - "cr gan learning complete representations for multi view generation"
    - "every smile is unique landmark guided diverse smile generation"
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "spectral normalization for generative adversarial networks"
    - "ganimation anatomically aware facial animation from a single image"
    - "non local neural networks"
    - "pose guided person image generation"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"

Connected through hyperedge HE-8 (induced by source paper "pcgan partition controlled human image generation"):
  with members:
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "pose guided person image generation"
    - "a generative model of people in clothing"
    - "deformable gans for pose based human image generation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "super fan integrated facial landmark localization and super resolution of real world low resolution faces in arbitrary poses with gans"
    - "disentangled person image generation"
    - "dualgan unsupervised dual learning for image to image translation"

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)