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: "efficient inference in occlusion aware generative models of images"
  Abstract: We present a generative model of images based on layering, in which image layers are individually generated, then composited from front to back. We are thus able to factor the appearance of an image into the appearance of individual objects within the image --- and additionally for each individual object, we can factor content from pose. Unlike prior work on layered models, we learn a shape prior for each object/layer, allowing the model to tease out which object is in front by looking for a consistent shape, without needing access to motion cues or any labeled data. We show that ordinary stochastic gradient variational bayes (SGVB), which optimizes our fully differentiable lower-bound on the log-likelihood, is sufficient to learn an interpretable representation of images. Finally we present experiments demonstrating the effectiveness of the model for inferring foreground and background objects in images.

Connected through hyperedge HE-1 (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"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "unsupervised discovery of parts structure and dynamics"
    - "multi object representation learning with iterative variational inference"
    - "unsupervised object segmentation by redrawing"

Connected through hyperedge HE-2 (induced by source paper "physics as inverse graphics joint unsupervised learning of objects and physics from video"):
  with members:
    - "unsupervised discovery of parts structure and dynamics"
    - "learning programmatically structured representations with perceptor gradients"
    - "object oriented dynamics predictor"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-3 (induced by source paper "unsupervised discovery of parts structure and dynamics"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "synthesizing images of humans in unseen poses"
    - "msnet mutual suppression network for disentangled video representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "disentangled sequential autoencoder"
    - "learning a physical long term predictor"
    - "visual interaction networks"
    - "neural expectation maximization"

Connected through hyperedge HE-4 (induced by source paper "context aware automatic occlusion removal"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "generative image inpainting with contextual attention"
    - "generative face completion"
    - "segan segmenting and generating the invisible"
    - "natural and effective obfuscation by head inpainting"
    - "eye in painting with exemplar generative adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "context encoders feature learning by inpainting"

Connected through hyperedge HE-5 (induced by source paper "learning segmentation masks with the independence prior"):
  with members:
    - "weakly supervised instance segmentation using class peak response"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "spectral normalization for generative adversarial networks"
    - "annotation free and one shot learning for instance segmentation of homogeneous object clusters"
    - "mine mutual information neural estimation"
    - "image generation from scene graphs"
    - "relation networks for object detection"

Connected through hyperedge HE-6 (induced by source paper "tracking by animation unsupervised learning of multi object attentive trackers"):
  with members:
    - "multiple hypothesis tracking algorithm for multi target multi camera tracking with disjoint views"
    - "features for multi target multi camera tracking and re identification"
    - "learning to decompose and disentangle representations for video prediction"
    - "unsupervised learning of disentangled representations from video"
    - "neural expectation maximization"
    - "multi target tracking in multiple non overlapping cameras using constrained dominant sets"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "deep network flow for multi object tracking"

Connected through hyperedge HE-7 (induced by source paper "lr gan layered recursive generative adversarial networks for image generation"):
  with members:
    - "generating images part by part with composite generative adversarial networks"
    - "improving variational inference with inverse autoregressive flow"
    - "energy based generative adversarial network"
    - "generating images with recurrent adversarial networks"
    - "learning what and where to draw"
    - "generative image modeling using style and structure adversarial networks"
    - "improved techniques for training gans"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-8 (induced by source paper "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"):
  with members:
    - "learning purposeful behaviour in the absence of rewards"
    - "deep exploration via bootstrapped dqn"
    - "understanding visual concepts with continuation learning"
    - "building machines that learn and think like people"
    - "attend infer repeat fast scene understanding with generative models"
    - "prioritized experience replay"
    - "deep convolutional inverse graphics network"
    - "language understanding for text based games using deep reinforcement learning"

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)