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: "genesis generative scene inference and sampling with object centric latent representations"
  Abstract: Generative latent-variable models are emerging as promising tools in robotics and reinforcement learning. Yet, even though tasks in these domains typically involve distinct objects, most state-of-the-art generative models do not explicitly capture the compositional nature of visual scenes. Two recent exceptions, MONet and IODINE, decompose scenes into objects in an unsupervised fashion. Their underlying generative processes, however, do not account for component interactions. Hence, neither of them allows for principled sampling of novel scenes. Here we present GENESIS, the first object-centric generative model of 3D visual scenes capable of both decomposing and generating scenes by capturing relationships between scene components. GENESIS parameterises a spatial GMM over images which is decoded from a set of object-centric latent variables that are either inferred sequentially in an amortised fashion or sampled from an autoregressive prior. We train GENESIS on several publicly available datasets and evaluate its performance on scene generation, decomposition, and semi-supervised learning.

Connected through hyperedge HE-1 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "towards a human like open domain chatbot"
    - "learning higher order logic programs"
    - "solving rubik s cube with a robot hand"
    - "on the measure of intelligence"
    - "entity abstraction in visual model based reinforcement learning"
    - "emergent systematic generalization in a situated agent"
    - "monet unsupervised scene decomposition and representation"

Connected through hyperedge HE-2 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "a layer based sequential framework for scene generation with gans"
    - "transformable bottleneck networks"
    - "generating multiple objects at spatially distinct locations"
    - "neural voxel renderer learning an accurate and controllable rendering tool"
    - "emergence of object segmentation in perturbed generative models"
    - "escaping plato s cave 3d shape from adversarial rendering"
    - "scene representation networks continuous 3d structure aware neural scene representations"

Connected through hyperedge HE-3 (induced by source paper "space unsupervised object oriented scene representation via spatial attention and decomposition"):
  with members:
    - "exploiting spatial invariance for scalable unsupervised object tracking"
    - "monet unsupervised scene decomposition and representation"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "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-4 (induced by source paper "scalor generative world models with scalable object representations"):
  with members:
    - "space unsupervised object oriented scene representation via spatial attention and decomposition"
    - "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"
    - "learning to decompose and disentangle representations for video prediction"
    - "tracking by animation unsupervised learning of multi object attentive trackers"
    - "hierarchical attentive recurrent tracking"

Connected through hyperedge HE-5 (induced by source paper "towards unsupervised learning of generative models for 3d controllable image synthesis"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "a layer based sequential framework for scene generation with gans"
    - "monet unsupervised scene decomposition and representation"
    - "singan learning a generative model from a single natural image"
    - "escaping plato s cave 3d shape from adversarial rendering"
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "structured3d a large photo realistic dataset for structured 3d modeling"
    - "multi object representation learning with iterative variational inference"

Connected through hyperedge HE-6 (induced by source paper "structured object aware physics prediction for video modeling and planning"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "physics as inverse graphics joint unsupervised learning of objects and physics from video"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "model based reinforcement learning for atari"
    - "multi object representation learning with iterative variational inference"
    - "relational action forecasting"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"

Connected through hyperedge HE-7 (induced by source paper "scalable object oriented sequential generative models"):
  with members:
    - "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"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "learning to decompose and disentangle representations for video prediction"
    - "tracking by animation unsupervised learning of multi object attentive trackers"
    - "hierarchical attentive recurrent tracking"
    - "neural expectation maximization"

Connected through hyperedge HE-8 (induced by source paper "imagine that leveraging emergent affordances for tool synthesis in reaching tasks"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "multi object representation learning with iterative variational inference"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "visual reinforcement learning with imagined goals"
    - "world models"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "affordancenet an end to end deep learning approach for object affordance detection"

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)