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: "towards open set identity preserving face synthesis"
  Abstract: We propose a framework based on Generative Adversarial Networks to disentangle the identity and attributes of faces, such that we can conveniently recombine different identities and attributes for identity preserving face synthesis in open domains. Previous identity preserving face synthesis processes are largely confined to synthesizing faces with known identities that are already in the training dataset. To synthesize a face with identity outside the training dataset, our framework requires one input image of that subject to produce an identity vector, and any other input face image to extract an attribute vector capturing, e.g., pose, emotion, illumination, and even the background. We then recombine the identity vector and the attribute vector to synthesize a new face of the subject with the extracted attribute. Our proposed framework does not need to annotate the attributes of faces in any way. It is trained with an asymmetric loss function to better preserve the identity and stabilize the training process. It can also effectively leverage large amounts of unlabeled training face images to further improve the fidelity of the synthesized faces for subjects that are not presented in the labeled training face dataset. Our experiments demonstrate the efficacy of the proposed framework. We also present its usage in a much broader set of applications including face frontalization, face attribute morphing, and face adversarial example detection.

Connected through hyperedge HE-1 (induced by source paper "fine grained image to image transformation towards visual recognition"):
  with members:
    - "a two step disentanglement method"
    - "hologan unsupervised learning of 3d representations from natural images"
    - "foreground aware image inpainting"
    - "carigan caricature generation through weakly paired adversarial learning"
    - "visual object networks image generation with disentangled 3d representation"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "cr gan learning complete representations for multi view generation"
    - "multi task convolutional neural network for pose invariant face recognition"

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"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"

Connected through hyperedge HE-3 (induced by source paper "example guided style consistent image synthesis from semantic labeling"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "multimodal unsupervised image to image translation"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "arbitrary style transfer with deep feature reshuffle"
    - "semi parametric image synthesis"
    - "dual attention network for scene segmentation"
    - "densepose dense human pose estimation in the wild"

Connected through hyperedge HE-4 (induced by source paper "high fidelity face manipulation with extreme pose and expression"):
  with members:
    - "pose agnostic cross spectral hallucination via disentangling independent factors"
    - "generating diverse high fidelity images with vq vae 2"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "semantic image synthesis with spatially adaptive normalization"
    - "dual variational generation for low shot heterogeneous face recognition"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "video to video synthesis"
    - "global and local consistent wavelet domain age synthesis"

Connected through hyperedge HE-5 (induced by source paper "mixnmatch multifactor disentanglement and encoding for conditional image generation"):
  with members:
    - "unsupervised part based disentangling of object shape and appearance"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "synthesizing images of humans in unseen poses"
    - "deformable generator network unsupervised disentanglement of appearance and geometry"
    - "multimodal unsupervised image to image translation"
    - "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery"

Connected through hyperedge HE-6 (induced by source paper "make a face towards arbitrary high fidelity face manipulation"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "on learning 3d face morphable model from in the wild images"
    - "pose guided human video generation"
    - "look at boundary a boundary aware face alignment algorithm"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "hallucinated iqa no reference image quality assessment via adversarial learning"
    - "self supervised learning of a facial attribute embedding from video"
    - "blind predicting similar quality map for image quality assessment"

Connected through hyperedge HE-7 (induced by source paper "learning disentangled representation for robust person re identification"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "exploring disentangled feature representation beyond face identification"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "human semantic parsing for person re identification"

Connected through hyperedge HE-8 (induced by source paper "synthesizing coupled 3d face modalities by trunk branch generative adversarial networks"):
  with members:
    - "meshgan non linear 3d morphable models of faces"
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "representation learning by rotating your faces"
    - "3dfacegan adversarial nets for 3d face representation generation and translation"
    - "combining 3d morphable models a large scale face and head model"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "semi supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model"
    - "arcface additive angular margin loss for deep face recognition"

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)