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: "advantages and limitations of using successor features for transfer in reinforcement learning"
  Abstract: One question central to Reinforcement Learning is how to learn a feature representation that supports algorithm scaling and re-use of learned information from different tasks. Successor Features approach this problem by learning a feature representation that satisfies a temporal constraint. We present an implementation of an approach that decouples the feature representation from the reward function, making it suitable for transferring knowledge between domains. We then assess the advantages and limitations of using Successor Features for transfer.

Connected through hyperedge HE-1 (induced by source paper "successor features combine elements of model free and model based reinforcement learning"):
  with members:
    - "deepmdp learning continuous latent space models for representation learning"
    - "combined reinforcement learning via abstract representations"
    - "learning the reward function for a misspecified model"
    - "lipschitz continuity in model based reinforcement learning"
    - "is q learning provably efficient"
    - "value prediction network"
    - "near optimal behavior via approximate state abstraction"
    - "imagination augmented agents for deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "successor features support model based and model free reinforcement learning"):
  with members:
    - "world models"
    - "lipschitz continuity in model based reinforcement learning"
    - "value prediction network"
    - "near optimal behavior via approximate state abstraction"
    - "imagination augmented agents for deep reinforcement learning"
    - "successor features for transfer in reinforcement learning"
    - "self correcting models for model based reinforcement learning"
    - "deep reinforcement learning with successor features for navigation across similar environments"

Connected through hyperedge HE-3 (induced by source paper "better transfer learning with inferred successor maps"):
  with members:
    - "transfer in deep reinforcement learning using successor features and generalised policy improvement"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "universal successor features approximators"
    - "experience replay for continual learning"
    - "universal successor representations for transfer reinforcement learning"
    - "selective experience replay for lifelong learning"
    - "neural episodic control"
    - "distral robust multitask reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "source traces for temporal difference learning"):
  with members:
    - "successor features for transfer in reinforcement learning"
    - "deep reinforcement learning with successor features for navigation across similar environments"
    - "prioritized experience replay"

Connected through hyperedge HE-5 (induced by source paper "state2vec off policy successor features approximators"):
  with members:
    - "representation learning on graphs a reinforcement learning application"
    - "successor features for transfer in reinforcement learning"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-6 (induced by source paper "sdrl interpretable and data efficient deep reinforcement learningleveraging symbolic planning"):
  with members:
    - "strategic object oriented reinforcement learning"
    - "fast exploration with simplified models and approximately optimistic planning in model based reinforcement learning"
    - "robot representing and reasoning with knowledge from reinforcement learning"
    - "programmatically interpretable reinforcement learning"
    - "hierarchical imitation and reinforcement learning"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "explaining explanations an overview of interpretability of machine learning"
    - "eigenoption discovery through the deep successor representation"

Connected through hyperedge HE-7 (induced by source paper "transfer with model features in reinforcement learning"):
  with members:
    - "successor features for transfer in reinforcement learning"
    - "deep reinforcement learning with successor features for navigation across similar environments"
    - "adam a method for stochastic optimization"
    - "reinforcement learning a survey"

Connected through hyperedge HE-8 (induced by source paper "an optimal online method of selecting source policies for reinforcement learning"):
  with members:
    - "progressive neural networks"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "successor features for transfer in reinforcement learning"
    - "bayesian policy reuse"
    - "actor mimic deep multitask and transfer reinforcement learning"
    - "sample complexity of multi task 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.AI(Artificial Intelligence)