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: "value iteration networks"
  Abstract: We introduce the value iteration network (VIN): a fully differentiable neural network with a `planning module' embedded within. VINs can learn to plan, and are suitable for predicting outcomes that involve planning-based reasoning, such as policies for reinforcement learning. Key to our approach is a novel differentiable approximation of the value-iteration algorithm, which can be represented as a convolutional neural network, and trained end-to-end using standard backpropagation. We evaluate VIN based policies on discrete and continuous path-planning domains, and on a natural-language based search task. We show that by learning an explicit planning computation, VIN policies generalize better to new, unseen domains.

Connected through hyperedge HE-1 (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-2 (induced by source paper "learning functionally decomposed hierarchies for continuous control tasks"):
  with members:
    - "planning with goal conditioned policies"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "benchmarking model based reinforcement learning"
    - "universal planning networks"
    - "deepmind control suite"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "value propagation networks"
    - "learning multi level hierarchies with hindsight"

Connected through hyperedge HE-3 (induced by source paper "transferable task execution from pixels through deep planning domain learning"):
  with members:
    - "representing robot task plans as robust logical dynamical systems"
    - "prospection interpretable plans from language by predicting the future"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "stripstream integrating symbolic planners and blackbox samplers"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "visual robot task planning"

Connected through hyperedge HE-4 (induced by source paper "efficient exploration in constrained environments with goal oriented reference path"):
  with members:
    - "combining optimal control and learning for visual navigation in novel environments"
    - "learning robotic assembly from cad"
    - "learning navigation behaviors end to end with autorl"
    - "state representation learning for control an overview"
    - "motion planning networks"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-5 (induced by source paper "learning navigation costs from demonstration in partially observable environments"):
  with members:
    - "differentiable algorithm networks for composable robot learning"
    - "path integral networks end to end differentiable optimal control"
    - "memory augmented control networks"
    - "qmdp net deep learning for planning under partial observability"
    - "reinforcement learning via recurrent convolutional neural networks"
    - "cognitive mapping and planning for visual navigation"
    - "watch this scalable cost function learning for path planning in urban environments"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-6 (induced by source paper "federated deep reinforcement learning"):
  with members:
    - "federated machine learning concept and applications"
    - "fully convolutional network with multi step reinforcement learning for image processing"
    - "extracting action sequences from texts based on deep reinforcement learning"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "federated multi task learning"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-7 (induced by source paper "scaling all goals updates in reinforcement learning using convolutional neural networks"):
  with members:
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "many goals reinforcement learning"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "time limits in reinforcement learning"
    - "action branching architectures for deep reinforcement learning"
    - "deep exploration via bootstrapped dqn"
    - "exploration a study of count based exploration for deep reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "adjust planning strategies to accommodate reinforcement learning agents"):
  with members:
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "semi parametric topological memory for navigation"
    - "a distributional perspective on reinforcement learning"
    - "prm rl long range robotic navigation tasks by combining reinforcement learning and sampling based planning"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "how transferable are features in deep neural networks"

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.AI(Artificial Intelligence)