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: "successor features for transfer in reinforcement learning"
  Abstract: Transfer in reinforcement learning refers to the notion that generalization should occur not only within a task but also across tasks. We propose a transfer framework for the scenario where the reward function changes between tasks but the environment's dynamics remain the same. Our approach rests on two key ideas: "successor features", a value function representation that decouples the dynamics of the environment from the rewards, and "generalized policy improvement", a generalization of dynamic programming's policy improvement operation that considers a set of policies rather than a single one. Put together, the two ideas lead to an approach that integrates seamlessly within the reinforcement learning framework and allows the free exchange of information across tasks. The proposed method also provides performance guarantees for the transferred policy even before any learning has taken place. We derive two theorems that set our approach in firm theoretical ground and present experiments that show that it successfully promotes transfer in practice, significantly outperforming alternative methods in a sequence of navigation tasks and in the control of a simulated robotic arm.

Connected through hyperedge HE-1 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "asking easy questions a user friendly approach to active reward learning"
    - "extending deep model predictive control with safety augmented value estimation from demonstrations"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "functional variational bayesian neural networks"
    - "causal confusion in imitation learning"
    - "a divergence minimization perspective on imitation learning methods"
    - "a simple baseline for bayesian uncertainty in deep learning"

Connected through hyperedge HE-2 (induced by source paper "hierarchically decoupled imitation for morphological transfer"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "dynamics aware unsupervised discovery of skills"
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "hierarchical reinforcement learning with advantage based auxiliary rewards"
    - "learning to control self assembling morphologies a study of generalization via modularity"
    - "skill transfer in deep reinforcement learning under morphological heterogeneity"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "hardware conditioned policies for multi robot transfer learning"

Connected through hyperedge HE-3 (induced by source paper "generalized hindsight for reinforcement learning"):
  with members:
    - "sub policy adaptation for hierarchical reinforcement learning"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "deep drone racing learning agile flight in dynamic environments"
    - "visual reinforcement learning with imagined goals"
    - "soft actor critic algorithms and applications"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "hindsight experience replay"

Connected through hyperedge HE-4 (induced by source paper "policy evaluation networks"):
  with members:
    - "a comparative analysis of expected and distributional reinforcement learning"
    - "the value function polytope in reinforcement learning"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "value prediction network"
    - "a distributional perspective on reinforcement learning"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "invariant causal prediction for block mdps"):
  with members:
    - "deepmdp learning continuous latent space models for representation learning"
    - "meta learning without memorization"
    - "attention privileged reinforcement learning for domain transfer"
    - "deepmind control suite"
    - "natural environment benchmarks for reinforcement learning"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "generalized hidden parameter mdps transferable model based rl in a handful of trials"):
  with members:
    - "synthesized policies for transfer and adaptation across tasks and environments"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "deepmdp learning continuous latent space models for representation learning"
    - "benchmarking model based reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "efficient transfer learning and online adaptation with latent variable models for continuous control"
    - "decoupling dynamics and reward for transfer learning"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

Connected through hyperedge HE-7 (induced by source paper "learning state abstractions for transfer in continuous control"):
  with members:
    - "state representation learning for control an overview"
    - "value prediction network"
    - "near optimal behavior via approximate state abstraction"
    - "distral robust multitask reinforcement learning"
    - "learning to reinforcement learn"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "a vector contraction inequality for rademacher complexities"
    - "state of the art control of atari games using shallow reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "contextual policy reuse using deep mixture models"):
  with members:
    - "model based reinforcement learning for atari"
    - "ac teach a bayesian actor critic method for policy learning with an ensemble of suboptimal teachers"
    - "context aware policy reuse"
    - "an optimal online method of selecting source policies for reinforcement learning"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "transfer from simulation to real world through learning deep inverse dynamics model"

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)