human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
  Abstract: We present a simple neural rendering architecture that helps variational autoencoders (VAEs) learn disentangled representations. Instead of the deconvolutional network typically used in the decoder of VAEs, we tile (broadcast) the latent vector across space, concatenate fixed X- and Y-"coordinate" channels, and apply a fully convolutional network with 1x1 stride. This provides an architectural prior for dissociating positional from non-positional features in the latent distribution of VAEs, yet without providing any explicit supervision to this effect. We show that this architecture, which we term the Spatial Broadcast decoder, improves disentangling, reconstruction accuracy, and generalization to held-out regions in data space. It provides a particularly dramatic benefit when applied to datasets with small objects. We also emphasize a method for visualizing learned latent spaces that helped us diagnose our models and may prove useful for others aiming to assess data representations. Finally, we show the Spatial Broadcast Decoder is complementary to state-of-the-art (SOTA) disentangling techniques and when incorporated improves their performance.

Connected through hyperedge HE-1 (induced by source paper "high fidelity synthesis with disentangled representation"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "weakly supervised disentanglement by pairwise similarities"
    - "infogan cr disentangling generative adversarial networks with contrastive regularizers"
    - "learning disentangled representations with reference based variational autoencoders"
    - "disentangling factors of variation using few labels"
    - "on the fairness of disentangled representations"
    - "flexibly fair representation learning by disentanglement"
    - "oogan disentangling gan with one hot sampling and orthogonal regularization"

Connected through hyperedge HE-2 (induced by source paper "deep variational luenberger type observer for stochastic video prediction"):
  with members:
    - "video prediction for precipitation nowcasting"
    - "stochastic prediction of multi agent interactions from partial observations"
    - "disentangling factors of variation using few labels"
    - "conditional flow variational autoencoders for structured sequence prediction"
    - "unsupervised discovery of parts structure and dynamics"
    - "recurrent kalman networks factorized inference in high dimensional deep feature spaces"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "state space lstm models with particle mcmc inference"

Connected through hyperedge HE-3 (induced by source paper "product kanerva machines factorized bayesian memory"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "monet unsupervised scene decomposition and representation"
    - "generalization of reinforcement learners with working and episodic memory"
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "metalearned neural memory"
    - "recurrent independent mechanisms"
    - "shaping belief states with generative environment models for rl"
    - "optimizing agent behavior over long time scales by transporting value"

Connected through hyperedge HE-4 (induced by source paper "space unsupervised object oriented scene representation via spatial attention and decomposition"):
  with members:
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "exploiting spatial invariance for scalable unsupervised object tracking"
    - "monet unsupervised scene decomposition and representation"
    - "multi object representation learning with iterative variational inference"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "a simple neural network module for relational reasoning"
    - "neural expectation maximization"
    - "continuously differentiable exponential linear units"

Connected through hyperedge HE-5 (induced by source paper "genesis generative scene inference and sampling with object centric latent representations"):
  with members:
    - "emergence of object segmentation in perturbed generative models"
    - "monet unsupervised scene decomposition and representation"
    - "are disentangled representations helpful for abstract visual reasoning"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "unsupervised discovery of parts structure and dynamics"
    - "multi object representation learning with iterative variational inference"
    - "unsupervised object segmentation by redrawing"
    - "shaping belief states with generative environment models for rl"

Connected through hyperedge HE-6 (induced by source paper "monet unsupervised scene decomposition and representation"):
  with members:
    - "learning to infer and execute 3d shape programs"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "adversarial scene editing automatic object removal from weak supervision"
    - "path aggregation network for instance segmentation"
    - "tracking emerges by colorizing videos"
    - "metacontrol for adaptive imagination based optimization"

Connected through hyperedge HE-7 (induced by source paper "multi object representation learning with iterative variational inference"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "iterative amortized inference"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "yolov3 an incremental improvement"
    - "towards a definition of disentangled representations"
    - "neural expectation maximization"

Connected through hyperedge HE-8 (induced by source paper "disentangled relational representations for explaining and learning from demonstration"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "monet unsupervised scene decomposition and representation"
    - "multi object representation learning with iterative variational inference"
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "interpretable latent spaces for learning from demonstration"
    - "isolating sources of disentanglement in variational autoencoders"
    - "unsupervised learning of disentangled representations from video"
    - "a simple neural network module for relational reasoning"

Question: Please tell me which class the center node 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)