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 mammogram synthesis using progressive generative adversarial networks"
  Abstract: The ability to generate synthetic medical images is useful for data augmentation, domain transfer, and out-of-distribution detection. However, generating realistic, high-resolution medical images is challenging, particularly for Full Field Digital Mammograms (FFDM), due to the textural heterogeneity, fine structural details and specific tissue properties. In this paper, we explore the use of progressively trained generative adversarial networks (GANs) to synthesize mammograms, overcoming the underlying instabilities when training such adversarial models. This work is the first to show that generation of realistic synthetic medical images is feasible at up to 1280x1024 pixels, the highest resolution achieved for medical image synthesis, enabling visualizations within standard mammographic hanging protocols. We hope this work can serve as a useful guide and facilitate further research on GANs in the medical imaging domain.

Connected through hyperedge HE-1 (induced by source paper "perceptual image super resolution with progressive adversarial network"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "analyzing and improving the image quality of stylegan"
    - "deep learning for image super resolution a survey"
    - "zoom to learn learn to zoom"
    - "camera lens super resolution"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "fast accurate and lightweight super resolution with cascading residual network"

Connected through hyperedge HE-2 (induced by source paper "domain specific image super resolution with progressive adversarial network"):
  with members:
    - "zoom to learn learn to zoom"
    - "camera lens super resolution"
    - "adversarial noise layer regularize neural network by adding noise"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-3 (induced by source paper "curriculum learning for deep generative models with clustering"):
  with members:
    - "pioneer networks progressively growing generative autoencoder"
    - "pros and cons of gan evaluation measures"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "which training methods for gans do actually converge"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"

Connected through hyperedge HE-4 (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"
    - "disentangling multiple conditional inputs in gans"
    - "progressive growing of gans for improved quality stability and variation"
    - "high resolution deep convolutional generative adversarial networks"
    - "face aging with conditional generative adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-5 (induced by source paper "decompose to manipulate manipulable object synthesis in 3d medical images with structured image decomposition"):
  with members:
    - "on the convergence of adam and beyond"
    - "factorised spatial representation learning application in semi supervised myocardial segmentation"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "scargan chained generative adversarial networks to simulate pathological tissue on cardiovascular mr scans"
    - "conditional infilling gans for data augmentation in mammogram classification"
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "spectral normalization for generative adversarial networks"
    - "learning hierarchical semantic image manipulation through structured representations"

Connected through hyperedge HE-6 (induced by source paper "towards generative adversarial networks as a new paradigm for radiology education"):
  with members:
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "high resolution medical image synthesis using progressively grown generative adversarial networks"
    - "image synthesis in multi contrast mri with conditional generative adversarial networks"
    - "freehand ultrasound image simulation with spatially conditioned generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "a classification based study of covariate shift in gan distributions"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "synthetic medical images from dual generative adversarial 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)