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: "coco gan generation by parts via conditional coordinating"
  Abstract: Humans can only interact with part of the surrounding environment due to biological restrictions. Therefore, we learn to reason the spatial relationships across a series of observations to piece together the surrounding environment. Inspired by such behavior and the fact that machines also have computational constraints, we propose \underline{CO}nditional \underline{CO}ordinate GAN (COCO-GAN) of which the generator generates images by parts based on their spatial coordinates as the condition. On the other hand, the discriminator learns to justify realism across multiple assembled patches by global coherence, local appearance, and edge-crossing continuity. Despite the full images are never generated during training, we show that COCO-GAN can produce \textbf{state-of-the-art-quality} full images during inference. We further demonstrate a variety of novel applications enabled by teaching the network to be aware of coordinates. First, we perform extrapolation to the learned coordinate manifold and generate off-the-boundary patches. Combining with the originally generated full image, COCO-GAN can produce images that are larger than training samples, which we called "beyond-boundary generation". We then showcase panorama generation within a cylindrical coordinate system that inherently preserves horizontally cyclic topology. On the computation side, COCO-GAN has a built-in divide-and-conquer paradigm that reduces memory requisition during training and inference, provides high-parallelism, and can generate parts of images on-demand.

Connected through hyperedge HE-1 (induced by source paper "a u net based discriminator for generative adversarial networks"):
  with members:
    - "consistency regularization for generative adversarial networks"
    - "analyzing and improving the image quality of stylegan"
    - "mixmatch a holistic approach to semi supervised learning"
    - "cgans with projection discriminator"
    - "the gan landscape losses architectures regularization and normalization"
    - "self supervised gans via auxiliary rotation loss"
    - "pa gan improving gan training by progressive augmentation"
    - "on line adaptative curriculum learning for gans"

Connected through hyperedge HE-2 (induced by source paper "bridging the gap between ai and healthcare sides towards developing clinically relevant ai powered diagnosis systems"):
  with members:
    - "learning more with less conditional pggan based data augmentation for brain metastases detection using highly rough annotation on mr images"
    - "enforcing constraints for interpolation and extrapolation in generative adversarial networks"
    - "learning more with less gan based medical image augmentation"
    - "use net incorporating squeeze and excitation blocks into u net for prostate zonal segmentation of multi institutional mri datasets"
    - "lung nodule classification using deep local global networks"
    - "synthesizing diverse lung nodules wherever massively 3d multi conditional gan based ct image augmentation for object detection"
    - "adaptive weighting multi field of view cnn for semantic segmentation in pathology"
    - "towards generative adversarial networks as a new paradigm for radiology education"

Connected through hyperedge HE-3 (induced by source paper "unsupervised semantic attribute discovery and control in generative models"):
  with members:
    - "analyzing and improving the image quality of stylegan"
    - "on variational bounds of mutual information"
    - "isolating sources of disentanglement in variational autoencoders"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "flow gan combining maximum likelihood and adversarial learning in generative models"
    - "improved techniques for training gans"

Connected through hyperedge HE-4 (induced by source paper "local class specific and global image level generative adversarial networks for semantic guided scene generation"):
  with members:
    - "seeing what a gan cannot generate"
    - "progressive pose attention transfer for person image generation"
    - "a layer based sequential framework for scene generation with gans"
    - "bridging the domain gap for ground to aerial image matching"
    - "singan learning a generative model from a single natural image"
    - "autogan neural architecture search for generative adversarial networks"
    - "few shot unsupervised image to image translation"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"

Connected through hyperedge HE-5 (induced by source paper "stabilizing gans with octave convolutions"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "spectral normalization for generative adversarial networks"
    - "darts differentiable architecture search"
    - "large scale gan training for high fidelity natural image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-6 (induced by source paper "what it thinks is important is important robustness transfers through input gradients"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "provably robust boosted decision stumps and trees against adversarial attacks"
    - "using pre training can improve model robustness and uncertainty"
    - "fixing the train test resolution discrepancy"
    - "adversarially robust transfer learning"
    - "minimally distorted adversarial examples with a fast adaptive boundary attack"

Connected through hyperedge HE-7 (induced by source paper "augmenting correlation structures in spatial data using deep generative models"):
  with members:
    - "generative adversarial networks for financial trading strategies fine tuning and combination"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "evolutionary generative adversarial networks"
    - "tile2vec unsupervised representation learning for spatially distributed data"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "point cloud gan"
    - "freehand ultrasound image simulation with spatially conditioned generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "progressive augmentation of gans"):
  with members:
    - "the gan landscape losses architectures regularization and normalization"
    - "self supervised gans via auxiliary rotation loss"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unsupervised representation learning by predicting image rotations"
    - "which training methods for gans do actually converge"
    - "on self modulation for generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"

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)