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: "make a face towards arbitrary high fidelity face manipulation"
  Abstract: Recent studies have shown remarkable success in face manipulation task with the advance of GANs and VAEs paradigms, but the outputs are sometimes limited to low-resolution and lack of diversity. #R##N#In this work, we propose Additive Focal Variational Auto-encoder (AF-VAE), a novel approach that can arbitrarily manipulate high-resolution face images using a simple yet effective model and only weak supervision of reconstruction and KL divergence losses. First, a novel additive Gaussian Mixture assumption is introduced with an unsupervised clustering mechanism in the structural latent space, which endows better disentanglement and boosts multi-modal representation with external memory. Second, to improve the perceptual quality of synthesized results, two simple strategies in architecture design are further tailored and discussed on the behavior of Human Visual System (HVS) for the first time, allowing for fine control over the model complexity and sample quality. Human opinion studies and new state-of-the-art Inception Score (IS) / Frechet Inception Distance (FID) demonstrate the superiority of our approach over existing algorithms, advancing both the fidelity and extremity of face manipulation task.

Connected through hyperedge HE-1 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images"):
  with members:
    - "maskgan towards diverse and interactive facial image manipulation"
    - "representation learning by rotating your faces"
    - "learning correspondence from the cycle consistency of time"
    - "semantic image synthesis with spatially adaptive normalization"
    - "face alignment in full pose range a 3d total solution"
    - "learning to predict layout to image conditional convolutions for semantic image synthesis"
    - "vision infused deep audio inpainting"
    - "cr gan learning complete representations for multi view generation"

Connected through hyperedge HE-2 (induced by source paper "everybody s talkin let me talk as you want"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "example guided style consistent image synthesis from semantic labeling"
    - "disentangling content and style via unsupervised geometry distillation"
    - "neural style preserving visual dubbing"
    - "geometry aware face completion and editing"
    - "high resolution talking face generation via mutual information approximation"
    - "lip movements generation at a glance"
    - "talking face generation by adversarially disentangled audio visual representation"

Connected through hyperedge HE-3 (induced by source paper "spoc spoofing camera fingerprints"):
  with members:
    - "fsgan subject agnostic face swapping and reenactment"
    - "faceforensics learning to detect manipulated facial images"
    - "source generator attribution via inversion"
    - "advfaces adversarial face synthesis"
    - "at gan a generative attack model for adversarial transferring on generative adversarial nets"
    - "exposing gan synthesized faces using landmark locations"
    - "detecting and simulating artifacts in gan fake images"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"

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)