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: "neural face editing with intrinsic image disentangling"
  Abstract: Traditional face editing methods often require a number of sophisticated and task specific algorithms to be applied one after the other --- a process that is tedious, fragile, and computationally intensive. In this paper, we propose an end-to-end generative adversarial network that infers a face-specific disentangled representation of intrinsic face properties, including shape (i.e. normals), albedo, and lighting, and an alpha matte. We show that this network can be trained on "in-the-wild" images by incorporating an in-network physically-based image formation module and appropriate loss functions. Our disentangling latent representation allows for semantically relevant edits, where one aspect of facial appearance can be manipulated while keeping orthogonal properties fixed, and we demonstrate its use for a number of facial editing applications.

Connected through hyperedge HE-1 (induced by source paper "copy and paste gan face hallucination from shaded thumbnails"):
  with members:
    - "identity preserving face recovery from stylized portraits"
    - "image super resolution using very deep residual channel attention networks"
    - "style aggregated network for facial landmark detection"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "attention aware face hallucination via deep reinforcement learning"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "single day outdoor photometric stereo"):
  with members:
    - "image to image translation with conditional adversarial networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"

Connected through hyperedge HE-3 (induced by source paper "semanticadv generating adversarial examples via attribute conditional image editing"):
  with members:
    - "meshadv adversarial meshes for visual recognition"
    - "semantic adversarial attacks parametric transformations that fool deep classifiers"
    - "big but imperceptible adversarial perturbations via semantic manipulation"
    - "spatially transformed adversarial examples"
    - "cosface large margin cosine loss for deep face recognition"
    - "accelerated training for massive classification via dynamic class selection"
    - "large scale gan training for high fidelity natural image synthesis"
    - "attacks meet interpretability attribute steered detection of adversarial samples"

Connected through hyperedge HE-4 (induced by source paper "learning structure via consensus for face segmentation and parsing"):
  with members:
    - "fsgan subject agnostic face swapping and reenactment"
    - "does generative face completion help face recognition"
    - "face mask extraction in video sequence"
    - "learning discriminators as energy networks in adversarial learning"
    - "adversarial structure matching loss for image segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "face completion with semantic knowledge and collaborative adversarial learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-5 (induced by source paper "sdit scalable and diverse cross domain image translation"):
  with members:
    - "controlling biases and diversity in diverse image to image translation"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "how good is my gan"
    - "learning to adapt structured output space for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-6 (induced by source paper "lifting autoencoders unsupervised learning of a fully disentangled 3d morphable model using deep non rigid structure from motion"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deep interpretable non rigid structure from motion"
    - "face alignment in full pose range a 3d total solution"
    - "visual object networks image generation with disentangled 3d representation"
    - "learning category specific mesh reconstruction from image collections"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "3d aware scene manipulation via inverse graphics"
    - "inverserendernet learning single image inverse rendering"

Connected through hyperedge HE-7 (induced by source paper "dual generator generative adversarial networks for multi domain image to image translation"):
  with members:
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "every smile is unique landmark guided diverse smile generation"
    - "dual discriminator generative adversarial nets"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "pose guided person image generation"
    - "generative face completion"

Connected through hyperedge HE-8 (induced by source paper "attention guided generative adversarial networks for unsupervised image to image translation"):
  with members:
    - "attribute guided sketch generation"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "video to video synthesis"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "attention gan for object transfiguration in wild images"
    - "mask aware photorealistic face attribute manipulation"
    - "large scale gan training for high fidelity natural image synthesis"

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)