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: "conditional image to image translation"
  Abstract: Image-to-image translation tasks have been widely investigated with Generative Adversarial Networks (GANs) and dual learning. However, existing models lack the ability to control the translated results in the target domain and their results usually lack of diversity in the sense that a fixed image usually leads to (almost) deterministic translation result. In this paper, we study a new problem, conditional image-to-image translation, which is to translate an image from the source domain to the target domain conditioned on a given image in the target domain. It requires that the generated image should inherit some domain-specific features of the conditional image from the target domain. Therefore, changing the conditional image in the target domain will lead to diverse translation results for a fixed input image from the source domain, and therefore the conditional input image helps to control the translation results. We tackle this problem with unpaired data based on GANs and dual learning. We twist two conditional translation models (one translation from A domain to B domain, and the other one from B domain to A domain) together for inputs combination and reconstruction while preserving domain independent features. We carry out experiments on men's faces from-to women's faces translation and edges to shoes&bags translations. The results demonstrate the effectiveness of our proposed method.

Connected through hyperedge HE-1 (induced by source paper "rsl net localising in satellite images from a radar on the ground"):
  with members:
    - "masking by moving learning distraction free radar odometry from pose information"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "adversarial training for adverse conditions robust metric localisation using appearance transfer"
    - "learning steerable filters for rotation equivariant cnns"
    - "polar transformer networks"
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning less is more 6d camera localization via 3d surface regression"

Connected through hyperedge HE-2 (induced by source paper "mix and match networks multi domain alignment for unpaired image to image translation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "learning to adapt structured output space for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "depth aware cnn for rgb d segmentation"
    - "multimodal unsupervised image to image translation"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "image to image translation for cross domain disentanglement"

Connected through hyperedge HE-3 (induced by source paper "transgaga geometry aware unsupervised image to image translation"):
  with members:
    - "carigans unpaired photo to caricature translation"
    - "weakly supervised discovery of geometry aware representation for 3d human pose estimation"
    - "synthesizing images of humans in unseen poses"
    - "hallucinated iqa no reference image quality assessment via adversarial learning"
    - "every smile is unique landmark guided diverse smile generation"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "unsupervised discovery of object landmarks as structural representations"

Connected through hyperedge HE-4 (induced by source paper "example guided scene image synthesis using masked spatial channel attention and patch based self supervision"):
  with members:
    - "example guided style consistent image synthesis from semantic labeling"
    - "semantic image synthesis with spatially adaptive normalization"
    - "photorealistic style transfer via wavelet transforms"
    - "video to video synthesis"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "3d point cloud generative adversarial network based on tree structured graph convolutions"):
  with members:
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "multiresolution tree networks for 3d point cloud processing"
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "dynamic graph cnn for learning on point clouds"
    - "vital visual tracking via adversarial learning"
    - "generative image inpainting with contextual attention"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

Connected through hyperedge HE-6 (induced by source paper "adversarial colorization of icons based on structure and color conditions"):
  with members:
    - "user guided deep anime line art colorization with conditional adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "semi supervised fusedgan for conditional image generation"
    - "began boundary equilibrium generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "unsupervised diverse colorization via generative adversarial networks"
    - "mocogan decomposing motion and content for video generation"

Connected through hyperedge HE-7 (induced by source paper "exploring explicit domain supervision for latent space disentanglement in unpaired image to image translation"):
  with members:
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "multimodal unsupervised image to image translation"
    - "attention gan for object transfiguration in wild images"
    - "unsupervised attention guided image to image translation"
    - "ganimation anatomically aware facial animation from a single image"
    - "drit diverse image to image translation via disentangled representations"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "combogan unrestrained scalability for image domain translation"

Connected through hyperedge HE-8 (induced by source paper "what and where to translate local mask based image to image translation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "batch instance normalization for adaptively style invariant neural networks"
    - "unsupervised attention guided image to image translation"
    - "ganimation anatomically aware facial animation from a single image"
    - "coloring with words guiding image colorization through text based palette generation"

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)