human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep automodulators"):
  Abstract: We introduce a new family of generative neural network models called automodulators. These autoencoder-like networks can faithfully reproduce individual real-world input images like autoencoders, and also generate a fused sample from an arbitrary combination of several such images, allowing "style-mixing" and other new applications. An automodulator decouples the data flow of decoder operations from statistical properties thereof and uses the latent vector to modulate the former by the latter, with a principled approach for mutual disentanglement of decoder layers. This is the first general-purpose model to successfully apply this principle on existing input images, whereas prior work has focused on random sampling in GANs. We introduce novel techniques for stable unsupervised training of the model on four high-resolution data sets. Besides style-mixing, we show state-of-the-art results in autoencoder comparison, and visual image quality nearly indistinguishable from state-of-the-art GANs. We expect the automodulator variants to become a useful building block for image applications and other data domains.

Includes members:
  Member "image2stylegan how to embed images into the stylegan latent space":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stylegan2 distillation for feed forward image manipulation")
  Member "implicit autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an explicit local and global representation disentanglement framework with applications in deep clustering and unsupervised object detection")
  Member "analyzing and improving the image quality of stylegan":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "large scale adversarial representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "generating diverse high fidelity images with vq vae 2":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "memory based graph networks")
  Member "towards photographic image manipulation with balanced growing of generative autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "metrics and methods for robustness evaluation of neural networks with generative models")
  Member "introvae introspective variational autoencoders for photographic image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling")
  Member "pioneer networks progressively growing generative autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "metrics and methods for robustness evaluation of neural networks with generative models")

Question: Please tell me which class the source paper 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)