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 "variation network learning high level attributes for controlled input manipulation"):
  Abstract: This paper presents the Variation Network (VarNet), a generative model providing means to manipulate the high-level attributes of a given input. The originality of our approach is that VarNet is not only capable of handling pre-defined attributes but can also learn the relevant attributes of the dataset by itself. These two settings can be easily combined which makes VarNet applicable for a wide variety of tasks. Further, VarNet has a sound probabilistic interpretation which grants us with a novel way to navigate in the latent spaces as well as means to control how the attributes are learned. We demonstrate experimentally that this model is capable of performing interesting input manipulation and that the learned attributes are relevant and interpretable.

Includes members:
  Member "learning discrete and continuous factors of data via alternating disentanglement":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semi supervised disentanglement with independent vector variational autoencoders")
  Member "lipschitz generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gans may have no nash equilibria")
  Member "diagnosing and enhancing vae models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ae ot gan training gans from data specific latent distribution")
  Member "challenging common assumptions in the unsupervised learning of disentangled representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "deep learning based decoding for constrained sequence codes":
    also connected through other hyperedges:
      (none)
  Member "style tokens unsupervised style modeling control and transfer in end to end speech synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unpaired image to speech synthesis with multimodal information bottleneck")
  Member "rednet residual encoder decoder network for indoor rgb d semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "real time fusion network for rgb d semantic segmentation incorporating unexpected obstacle detection for road driving images")
  Member "sampling generative networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "d vae a variational autoencoder for directed acyclic graphs")

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)