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: "instance level facial attributes transfer with geometry aware flow"
  Abstract: We address the problem of instance-level facial attribute transfer without paired training data, e.g. faithfully transferring the exact mustache from a source face to a target face. This is a more challenging task than the conventional semantic-level attribute transfer, which only preserves the generic attribute style instead of instance-level traits. We propose the use of geometry-aware flow, which serves as a well-suited representation for modeling the transformation between instance-level facial attributes. Specifically, we leverage the facial landmarks as the geometric guidance to learn the differentiable flows automatically, despite of the large pose gap existed. Geometry-aware flow is able to warp the source face attribute into the target face context and generate a warp-and-blend result. To compensate for the potential appearance gap between source and target faces, we propose a hallucination sub-network that produces an appearance residual to further refine the warp-and-blend result. Finally, a cycle-consistency framework consisting of both attribute transfer module and attribute removal module is designed, so that abundant unpaired face images can be used as training data. Extensive evaluations validate the capability of our approach in transferring instance-level facial attributes faithfully across large pose and appearance gaps. Thanks to the flow representation, our approach can readily be applied to generate realistic details on high-resolution images.

Connected through hyperedge HE-1 (induced by source paper "attribute driven spontaneous motion in unpaired image translation"):
  with members:
    - "carigans unpaired photo to caricature translation"
    - "soft gated warping gan for pose guided person image synthesis"
    - "multimodal unsupervised image to image translation"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "improving shape deformation in unsupervised image to image translation"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "semi parametric image synthesis"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "one shot face reenactment"):
  with members:
    - "dense intrinsic appearance flow for human pose transfer"
    - "semantic image synthesis with spatially adaptive normalization"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "facelet bank for fast portrait manipulation"
    - "synthesizing images of humans in unseen poses"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "a variational u net for conditional appearance and shape generation"
    - "reenactgan learning to reenact faces via boundary transfer"

Connected through hyperedge HE-3 (induced by source paper "maskgan towards diverse and interactive facial image manipulation"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "fine grained head pose estimation without keypoints"
    - "progressive growing of gans for improved quality stability and variation"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

Connected through hyperedge HE-4 (induced by source paper "biphasic learning of gans for high resolution image to image translation"):
  with members:
    - "cgans with projection discriminator"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "ganimation anatomically aware facial animation from a single image"
    - "mine mutual information neural estimation"
    - "learning a high fidelity pose invariant model for high resolution face frontalization"
    - "representation learning with contrastive predictive coding"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

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)