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: "mask aware photorealistic face attribute manipulation"
  Abstract: The task of face attribute manipulation has found increasing applications, but still remains challeng- ing with the requirement of editing the attributes of a face image while preserving its unique details. In this paper, we choose to combine the Variational AutoEncoder (VAE) and Generative Adversarial Network (GAN) for photorealistic image genera- tion. We propose an effective method to modify a modest amount of pixels in the feature maps of an encoder, changing the attribute strength contin- uously without hindering global information. Our training objectives of VAE and GAN are reinforced by the supervision of face recognition loss and cy- cle consistency loss for faithful preservation of face details. Moreover, we generate facial masks to en- force background consistency, which allows our training to focus on manipulating the foreground face rather than background. Experimental results demonstrate our method, called Mask-Adversarial AutoEncoder (M-AAE), can generate high-quality images with changing attributes and outperforms prior methods in detail preservation.

Connected through hyperedge HE-1 (induced by source paper "attentiongan unpaired image to image translation using attention guided generative adversarial networks"):
  with members:
    - "show attend and translate unsupervised image translation with self regularization and attention"
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "attention guided generative adversarial networks for unsupervised image to image translation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-2 (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"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unsupervised attention guided image to image translation"

Connected through hyperedge HE-3 (induced by source paper "relgan multi domain image to image translation via relative attributes"):
  with members:
    - "kg gan knowledge guided generative adversarial networks"
    - "cgans with projection discriminator"
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "the relativistic discriminator a key element missing from standard gan"
    - "multimodal unsupervised image to image translation"
    - "modular generative adversarial networks"
    - "differentiable learning to normalize via switchable normalization"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-4 (induced by source paper "editable generative adversarial networks generating and editing faces simultaneously"):
  with members:
    - "high quality bidirectional generative adversarial networks"
    - "on convergence and stability of gans"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "triple generative adversarial nets"
    - "style transfer for anime sketches with enhanced residual u net and auxiliary classifier gan"
    - "semi latent gan learning to generate and modify facial images from attributes"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "invertible conditional gans for image editing"

Connected through hyperedge HE-5 (induced by source paper "texture deformation based generative adversarial networks for face editing"):
  with members:
    - "deforming autoencoders unsupervised disentangling of shape and appearance"
    - "multimodal unsupervised image to image translation"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "rsgan face swapping and editing using face and hair representation in latent spaces"
    - "dna gan learning disentangled representations from multi attribute images"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "neural face editing with intrinsic image disentangling"
    - "unpaired image to image translation using cycle consistent adversarial networks"

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)