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: "photo to caricature translation on faces in the wild"
  Abstract: Recently, image-to-image translation has been made much progress owing to the success of conditional Generative Adversarial Networks (cGANs). However, it's still very challenging for translation tasks with the requirement of high-level visual information conversion, such as photo-to-caricature translation that requires satire, exaggeration, lifelikeness and artistry. We present an approach for learning to translate faces in the wild from the source photo domain to the target caricature domain with different styles, which can also be used for other high-level image-to-image translation tasks. In order to capture global structure with local statistics while translation, we design a dual pathway model of cGAN with one global discriminator and one patch discriminator. Beyond standard convolution (Conv), we propose a new parallel convolution (ParConv) to construct Parallel Convolutional Neural Networks (ParCNNs) for both global and patch discriminators, which can combine the information from previous layer with the current layer. For generator, we provide three more extra losses in association with adversarial loss to constrain consistency for generated output itself and with the target. Also the style can be controlled by the input style info vector. Experiments on photo-to-caricature translation of faces in the wild show considerable performance gain of our proposed method over state-of-the-art translation methods as well as its potential real applications.

Connected through hyperedge HE-1 (induced by source paper "warpgan automatic caricature generation"):
  with members:
    - "carigans unpaired photo to caricature translation"
    - "caricatureshop personalized and photorealistic caricature sketching"
    - "carigan caricature generation through weakly paired adversarial learning"
    - "soft gated warping gan for pose guided person image synthesis"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "multimodal unsupervised image to image translation"
    - "sphereface deep hypersphere embedding for face recognition"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-2 (induced by source paper "carigan caricature generation through weakly paired adversarial learning"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "stochastic adversarial video prediction"
    - "spectral normalization for generative adversarial networks"
    - "which training methods for gans do actually converge"
    - "began boundary equilibrium generative adversarial networks"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "high quality facial photo sketch synthesis using multi adversarial networks"):
  with members:
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "face synthesis from visual attributes via sketch using conditional vaes and gans"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "unsupervised creation of parameterized avatars"
    - "in2i unsupervised multi image to image translation using generative adversarial networks"
    - "towards realistic face photo sketch synthesis via composition aided gans"
    - "image de raining using a conditional generative adversarial network"

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)