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: "learning latent dynamics for planning from pixels"
  Abstract: Planning has been very successful for control tasks with known environment dynamics. To leverage planning in unknown environments, the agent needs to learn the dynamics from interactions with the world. However, learning dynamics models that are accurate enough for planning has been a long-standing challenge, especially in image-based domains. We propose the Deep Planning Network (PlaNet), a purely model-based agent that learns the environment dynamics from images and chooses actions through fast online planning in latent space. To achieve high performance, the dynamics model must accurately predict the rewards ahead for multiple time steps. We approach this using a latent dynamics model with both deterministic and stochastic transition components. Moreover, we propose a multi-step variational inference objective that we name latent overshooting. Using only pixel observations, our agent solves continuous control tasks with contact dynamics, partial observability, and sparse rewards, which exceed the difficulty of tasks that were previously solved by planning with learned models. PlaNet uses substantially fewer episodes and reaches final performance close to and sometimes higher than strong model-free algorithms.

Connected through hyperedge HE-1 (induced by source paper "probabilistic future prediction for video scene understanding"):
  with members:
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "model based reinforcement learning for atari"
    - "adversarial video generation on complex datasets"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning plannable representations with causal infogan"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "world models"

Connected through hyperedge HE-2 (induced by source paper "neuroevolution of self interpretable agents"):
  with members:
    - "rotation translation and cropping for zero shot generalization"
    - "skin lesion classification using cnns with patch based attention and diagnosis guided loss weighting"
    - "improving deep neuroevolution via deep innovation protection"
    - "investigating generalisation in continuous deep reinforcement learning"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "emergent systematic generalization in a situated agent"
    - "continual learning with hypernetworks"
    - "learning to generalize from sparse and underspecified rewards"

Connected through hyperedge HE-3 (induced by source paper "stochastic latent residual video prediction"):
  with members:
    - "videoflow a conditional flow based model for stochastic video generation"
    - "improved conditional vrnns for video prediction"
    - "cubic lstms for video prediction"
    - "unsupervised learning of object structure and dynamics from videos"
    - "scaling autoregressive video models"
    - "video to video synthesis"
    - "hierarchical long term video prediction without supervision"
    - "stochastic adversarial video prediction"

Connected through hyperedge HE-4 (induced by source paper "action for better prediction"):
  with members:
    - "self supervised exploration via disagreement"
    - "curious ilqr resolving uncertainty in model based rl"
    - "model based reinforcement learning for atari"
    - "manipulation by feel touch based control with deep predictive models"
    - "unsupervised minimax adversarial curiosity generative adversarial networks and predictability minimization"
    - "robonet large scale multi robot learning"
    - "learning predictive models from observation and interaction"
    - "exploration by random network distillation"

Connected through hyperedge HE-5 (induced by source paper "relational state space model for stochastic multi object systems"):
  with members:
    - "stochastic prediction of multi agent interactions from partial observations"
    - "graph normalizing flows"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"
    - "emerging convolutions for generative normalizing flows"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "state space lstm models with particle mcmc inference"

Connected through hyperedge HE-6 (induced by source paper "ready policy one world building through active learning"):
  with members:
    - "deep dynamics models for learning dexterous manipulation"
    - "learning to predict without looking ahead world models without forward prediction"
    - "self supervised exploration via disagreement"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "demystifying active inference"
    - "model based reinforcement learning for atari"
    - "dream to control learning behaviors by latent imagination"
    - "benchmarking model based reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "learning robotic manipulation through visual planning and acting"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "unsupervised state representation learning in atari"
    - "deepmdp learning continuous latent space models for representation learning"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "composable planning with attributes"

Connected through hyperedge HE-8 (induced by source paper "learning predictive representations for deformable objects using contrastive estimation"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "deep imitation learning of sequential fabric smoothing policies"
    - "contrastive multiview coding"
    - "learning robotic manipulation through visual planning and acting"
    - "learning to manipulate deformable objects without demonstrations"
    - "deep transfer learning of pick points on fabric for robot bed making"
    - "learning dexterous in hand manipulation"
    - "learning plannable representations with causal infogan"

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)