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: "high resolution deep convolutional generative adversarial networks"
  Abstract: Generative Adversarial Networks (GANs) convergence in a high-resolution setting with a computational constrain of GPU memory capacity (from 12GB to 24 GB) has been beset with difficulty due to the known lack of convergence rate stability. In order to boost network convergence of DCGAN (Deep Convolutional Generative Adversarial Networks) and achieve good-looking high-resolution results we propose a new layered network structure, HR-DCGAN, that incorporates current state-of-the-art techniques for this effect. A novel dataset, CZ Faces (CZF), containing human faces from different ethnical groups in a wide variety of illumination conditions and image resolutions is introduced. We conduct extensive experiments on CelebA and CZF.

Connected through hyperedge HE-1 (induced by source paper "doctor of crosswise reducing over parametrization in neural networks"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "revisiting random binning features fast convergence and strong parallelizability"
    - "autoaugment learning augmentation policies from data"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "mckernel approximate kernel expansions in log linear time through randomization"
    - "progressive growing of gans for improved quality stability and variation"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "mask embedding in conditional gan for guided synthesis of high resolution images"):
  with members:
    - "matching thermal to visible face images using a semantic guided generative adversarial network"
    - "singan learning a generative model from a single natural image"
    - "image synthesis in multi contrast mri with conditional generative adversarial networks"
    - "high resolution mammogram synthesis using progressive generative adversarial networks"
    - "disentangling multiple conditional inputs in gans"
    - "progressive growing of gans for improved quality stability and variation"
    - "face aging with conditional generative adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (induced by source paper "streetscape augmentation using generative adversarial networks insights related to health and wellbeing"):
  with members:
    - "few shot unsupervised image to image translation"
    - "multimodal unsupervised image to image translation"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "toward multimodal image to image translation"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "amortised map inference for image super resolution"
    - "deep learning the city quantifying urban perception at a global scale"

Connected through hyperedge HE-4 (induced by source paper "segmentation of instances by hashing"):
  with members:
    - "mckernel approximate kernel expansions in log linear time through randomization"
    - "progressive growing of gans for improved quality stability and variation"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "towards accurate multi person pose estimation in the wild"
    - "rethinking atrous convolution for semantic image segmentation"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "improved techniques for training gans"

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)