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 "an explicit local and global representation disentanglement framework with applications in deep clustering and unsupervised object detection"):
  Abstract: There are several benefits from learning disentangled representations, including interpretability, compositionality and generalisation to new tasks. Disentanglement could be done by imposing an inductive bias based on prior knowledge. Different priors make different structural assumptions about the representations. For instance, priors with granularity can lead to representation to describe data at different scales. A notable example is in the visual domain where there are multiple scales of the variation in the data. Hence, learning representation at different scales will be useful for different tasks. In this work, we propose a framework, called SPLIT, which allows us to disentangle local and global information into two separate sets of latent variables within the variational autoencoder (VAE) framework. Our framework adds an extra generative assumption to the VAE by requiring a subset of the latent variables to generate an auxiliary set of observable data. This set of data, which contains only local information, can be obtained via a transformation of the original data that removes global information. Three different flavours of VAE with different generative assumptions were examined in our experiments. We show that the framework can be effectively used to disentangle local and global information within these models. Benefits of the framework are demonstrated through multiple downstream representation learning problems. The framework can unlock the potential of these VAE models in the tasks of style transfer, deep clustering and unsupervised object detection with a simple modification to existing VAE models. Finally, we review cognitive neuroscience literature regarding disentanglement in human visual perception. The code for our experiments can be found at this https URL.

Includes members:
  Member "hologan unsupervised learning of 3d representations from natural images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")
  Member "implicit autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep automodulators")
  Member "explicit disentanglement of appearance and perspective in generative models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convolutional stn for weakly supervised object localization and beyond")
  Member "an explicitly relational neural network architecture":
    also connected through other hyperedges:
      (none)
  Member "unsupervised learning of object landmarks through conditional image generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "chained representation cycling learning to estimate 3d human pose and shape by cycling between representations")
  Member "isolating sources of disentanglement in variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "low resource knowledge grounded dialogue generation")
  Member "learning latent representations in neural networks for clustering through pseudo supervision and graph based activity regularization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "softmax based classification is k means clustering formal proof consequences for adversarial attacks and improvement through centroid based tailoring")
  Member "deep unsupervised clustering with gaussian mixture variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")

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)