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: "on the effectiveness of least squares generative adversarial networks"
  Abstract: Unsupervised learning with generative adversarial networks (GANs) has proven hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cross entropy loss function. However, we found that this loss function may lead to the vanishing gradients problem during the learning process. To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator. We show that minimizing the objective function of LSGAN yields minimizing the Pearson $\chi^2$ divergence. We also present a theoretical analysis about the properties of LSGANs and $\chi^2$ divergence. There are two benefits of LSGANs over regular GANs. First, LSGANs are able to generate higher quality images than regular GANs. Second, LSGANs perform more stable during the learning process. For evaluating the image quality, we train LSGANs on several datasets including LSUN and a cat dataset, and the experimental results show that the images generated by LSGANs are of better quality than the ones generated by regular GANs. Furthermore, we evaluate the stability of LSGANs in two groups. One is to compare between LSGANs and regular GANs without gradient penalty. We conduct three experiments, including Gaussian mixture distribution, difficult architectures, and a new proposed method --- datasets with small variance, to illustrate the stability of LSGANs. The other one is to compare between LSGANs with gradient penalty and WGANs with gradient penalty (WGANs-GP). The experimental results show that LSGANs with gradient penalty succeed in training for all the difficult architectures used in WGANs-GP, including 101-layer ResNet.

Connected through hyperedge HE-1 (induced by source paper "a novel framework for selection of gans for an application"):
  with members:
    - "a review on generative adversarial networks algorithms theory and applications"
    - "the six fronts of the generative adversarial networks"
    - "generative adversarial networks a survey and taxonomy"
    - "stabilizing generative adversarial network training a survey"
    - "how generative adversarial networks and their variants work an overview"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "improving generalization and stability of generative adversarial networks"
    - "adversarial video generation on complex datasets"

Connected through hyperedge HE-2 (induced by source paper "endol2h deep super resolution for capsule endoscopy"):
  with members:
    - "frame recurrent video super resolution"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "liquid warping gan a unified framework for human motion imitation appearance transfer and novel view synthesis"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "video to video synthesis"
    - "soft gated warping gan for pose guided person image synthesis"
    - "synthesizing images of humans in unseen poses"
    - "view extrapolation of human body from a single image"
    - "a variational u net for conditional appearance and shape generation"
    - "dense pose transfer"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-4 (induced by source paper "relgan multi domain image to image translation via relative attributes"):
  with members:
    - "kg gan knowledge guided generative adversarial networks"
    - "cgans with projection discriminator"
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "the relativistic discriminator a key element missing from standard gan"
    - "multimodal unsupervised image to image translation"
    - "modular generative adversarial networks"
    - "mask aware photorealistic face attribute manipulation"
    - "differentiable learning to normalize via switchable normalization"

Connected through hyperedge HE-5 (induced by source paper "disentangle align and fuse for multimodal and zero shot image segmentation"):
  with members:
    - "robust multimodal brain tumor segmentation via feature disentanglement and gated fusion"
    - "theory and evaluation metrics for learning disentangled representations"
    - "transgaga geometry aware unsupervised image to image translation"
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "unsupervised deformable registration for multi modal images via disentangled representations"
    - "semantic image synthesis with spatially adaptive normalization"
    - "representation disentanglement for multi task learning with application to fetal ultrasound"
    - "factorised spatial representation learning application in semi supervised myocardial segmentation"

Connected through hyperedge HE-6 (induced by source paper "predicting novel views using generative adversarial query network"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "fast view synthesis with deep stereo vision"
    - "stereo magnification learning view synthesis using multiplane images"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "cvae gan fine grained image generation through asymmetric training"
    - "unrolled generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "visual enhancement of cone beam ct by use of cyclegan"):
  with members:
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "deep learning in radiology an overview of the concepts and a survey of the state of the art"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "deep mr to ct synthesis using unpaired data"
    - "instance normalization the missing ingredient for fast stylization"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "temporal consistency objectives regularize the learning of disentangled representations"):
  with members:
    - "unsupervised deformable registration for multi modal images via disentangled representations"
    - "are disentangled representations helpful for abstract visual reasoning"
    - "joint learning of motion estimation and segmentation for cardiac mr image sequences"
    - "recurrent neural networks for aortic image sequence segmentation with sparse annotations"
    - "learning to decompose and disentangle representations for video prediction"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "u net convolutional networks for biomedical image segmentation"
    - "cyclical learning rates for training neural networks"

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)