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 "adversarial training of partially invertible variational autoencoders"):
  Abstract: Adversarial generative image models yield outstanding sample quality, but suffer from two drawbacks: (i) they mode-drop, \ie, do not cover the full support of the target distribution, and (ii) they do not allow for likelihood evaluations on held-out data. Conversely, maximum likelihood estimation encourages models to cover the full support of the training data, but yields poor samples. To address these mutual shortcomings, we propose a generative model that can be jointly trained with both procedures. In our approach, the conditional independence assumption typically made in variational autoencoders is relaxed by leveraging invertible models. This leads to improved sample quality, as well as improved likelihood on held-out data. Our model significantly improves on existing hybrid models, yielding GAN-like samples, and IS and FID scores that are competitive with fully adversarial models, while offering likelihoods measures on held-out data comparable to recent likelihood-based methods.

Includes members:
  Member "improving generalization and stability of generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a novel framework for selection of gans for an application")
  Member "cgans with projection discriminator":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object centric image generation from layouts")
  Member "spectral normalization for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "gans trained by a two time scale update rule converge to a nash equilibrium":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "modulating early visual processing by language":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "it takes only two adversarial generator encoder networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lia latently invertible autoencoder with adversarial learning")
  Member "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adaptive density estimation for generative models")
  Member "auxiliary guided autoregressive variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "nestedvae isolating common factors via weak supervision")

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.CV(Computer Vision and Pattern Recognition)