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 "bridging disentanglement with independence and conditional independence via mutual information for representation learning"):
  Abstract: Existing works on disentangled representation learning usually lie on a common assumption: all factors in disentangled representations should be independent. This assumption is about the inner property of disentangled representations, while ignoring their relation with external data. To tackle this problem, we propose another assumption to establish an important relation between data and its disentangled representations via mutual information: the mutual information between each factor of disentangled representations and data should be invariant to other factors. We formulate this assumption into mathematical equations, and theoretically bridge it with independence and conditional independence of factors. Meanwhile, we show that conditional independence is satisfied in encoders of VAEs due to factorized noise in reparameterization. To highlight the importance of our proposed assumption, we show in experiments that violating the assumption leads to dramatic decline of disentanglement. Based on this assumption, we further propose to split the deeper layers in encoder to ensure parameters in these layers are not shared for different factors. The proposed encoder, called Split Encoder, can be applied into models that penalize total correlation, and shows significant improvement in unsupervised learning of disentangled representations and reconstructions.

Includes members:
  Member "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "high fidelity synthesis with disentangled representation")
  Member "on the fairness of disentangled representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "competitive training of mixtures of independent deep generative models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weakly supervised disentanglement without compromises")
  Member "curiosity driven exploration of learned disentangled goal spaces":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "automatic curriculum learning for deep rl a short survey")
  Member "improving generalization for abstract reasoning tasks using disentangled feature representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "are disentangled representations helpful for abstract visual reasoning")
  Member "recent advances in autoencoder based representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "towards a definition of disentangled representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weakly supervised disentanglement without compromises")
  Member "guiding infogan with semi supervision":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unpaired pose guided human image generation")

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)