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: "instagan instance aware image to image translation"
  Abstract: Unsupervised image-to-image translation has gained considerable attention due to the recent impressive progress based on generative adversarial networks (GANs). However, previous methods often fail in challenging cases, in particular, when an image has multiple target instances and a translation task involves significant changes in shape, e.g., translating pants to skirts in fashion images. To tackle the issues, we propose a novel method, coined instance-aware GAN (InstaGAN), that incorporates the instance information (e.g., object segmentation masks) and improves multi-instance transfiguration. The proposed method translates both an image and the corresponding set of instance attributes while maintaining the permutation invariance property of the instances. To this end, we introduce a context preserving loss that encourages the network to learn the identity function outside of target instances. We also propose a sequential mini-batch inference/training technique that handles multiple instances with a limited GPU memory and enhances the network to generalize better for multiple instances. Our comparative evaluation demonstrates the effectiveness of the proposed method on different image datasets, in particular, in the aforementioned challenging cases. Code and results are available in this https URL

Connected through hyperedge HE-1 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "pq net a generative part seq2seq network for 3d shapes"
    - "object driven text to image synthesis via adversarial training"
    - "cvxnets learnable convex decomposition"
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"

Connected through hyperedge HE-2 (induced by source paper "image to image translation with text guidance"):
  with members:
    - "local class specific and global image level generative adversarial networks for semantic guided scene generation"
    - "singan learning a generative model from a single natural image"
    - "object driven text to image synthesis via adversarial training"
    - "manigan text guided image manipulation"
    - "controlling style and semantics in weakly supervised image generation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "specifying object attributes and relations in interactive scene generation"
    - "inferring semantic layout for hierarchical text to image synthesis"

Connected through hyperedge HE-3 (induced by source paper "can giraffes become birds an evaluation of image to image translation for data generation"):
  with members:
    - "an efficient and layout independent automatic license plate recognition system based on the yolo detector"
    - "anda a novel data augmentation technique applied to salient object detection"
    - "masking salient object detection a mask region based convolutional neural network analysis for segmentation of salient objects"
    - "semantic aware grad gan for virtual to real urban scene adaption"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-4 (induced by source paper "using simulated data to generate images of climate change"):
  with members:
    - "visualizing the consequences of climate change using cycle consistent adversarial networks"
    - "learning to adapt structured output space for semantic segmentation"
    - "megadepth learning single view depth prediction from internet photos"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "cycada cycle consistent adversarial domain adaptation"
    - "carla an open urban driving simulator"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-5 (induced by source paper "cycle in cycle generative adversarial networks for keypoint guided image generation"):
  with members:
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "gazecorrection self guided eye manipulation in the wild using self supervised generative adversarial networks"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cascade attention guided residue learning gan for cross modal translation"
    - "attention guided generative adversarial networks for unsupervised image to image translation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "video to video synthesis"
    - "cross view image synthesis using conditional gans"

Connected through hyperedge HE-6 (induced by source paper "learning structure appearance joint embedding for indoor scene image synthesis"):
  with members:
    - "learning to reconstruct 3d manhattan wireframes from a single image"
    - "semantic image synthesis with spatially adaptive normalization"
    - "end to end wireframe parsing"
    - "learning to predict layout to image conditional convolutions for semantic image synthesis"
    - "learning attraction field representation for robust line segment detection"
    - "multimodal unsupervised image to image translation"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-7 (induced by source paper "cali sketch stroke calibration and completion for high quality face image generation from poorly drawn sketches"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "csgan cyclic synthesized generative adversarial networks for image to image transformation"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "attentiongan unpaired image to image translation using attention guided generative adversarial networks"):
  with members:
    - "show attend and translate unsupervised image translation with self regularization and attention"
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "attention guided generative adversarial networks for unsupervised image to image translation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "multimodal unsupervised image to image translation"

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.LG(Machine Learning)