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: "universal successor representations for transfer reinforcement learning"
  Abstract: The objective of transfer reinforcement learning is to generalize from a set of previous tasks to unseen new tasks. In this work, we focus on the transfer scenario where the dynamics among tasks are the same, but their goals differ. Although general value function (Sutton et al., 2011) has been shown to be useful for knowledge transfer, learning a universal value function can be challenging in practice. To attack this, we propose (1) to use universal successor representations (USR) to represent the transferable knowledge and (2) a USR approximator (USRA) that can be trained by interacting with the environment. Our experiments show that USR can be effectively applied to new tasks, and the agent initialized by the trained USRA can achieve the goal considerably faster than random initialization.

Connected through hyperedge HE-1 (induced by source paper "learning action transferable policy with action embedding"):
  with members:
    - "transfer in deep reinforcement learning using successor features and generalised policy improvement"
    - "learning action representations for reinforcement learning"
    - "the natural language of actions"
    - "domain adaptation for reinforcement learning on the atari"
    - "combined reinforcement learning via abstract representations"
    - "learning latent dynamics for planning from pixels"
    - "deep contextualized word representations"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-2 (induced by source paper "learning transferable domain priors for safe exploration in reinforcement learning"):
  with members:
    - "transfer in deep reinforcement learning using successor features and generalised policy improvement"
    - "diverse exploration for fast and safe policy improvement"
    - "sample efficient reinforcement learning through transfer and architectural priors"
    - "learn what not to learn action elimination with deep reinforcement learning"
    - "investigating human priors for playing video games"
    - "kickstarting deep reinforcement learning"
    - "an optimal online method of selecting source policies for reinforcement learning"
    - "constrained policy optimization"

Connected through hyperedge HE-3 (induced by source paper "gamma nets generalizing value estimation over timescale"):
  with members:
    - "separating value functions across time scales"
    - "dopamine a research framework for deep reinforcement learning"
    - "meta gradient reinforcement learning"
    - "unicorn continual learning with a universal off policy agent"
    - "a distributional perspective on reinforcement learning"
    - "successor features for transfer in reinforcement learning"
    - "learning values across many orders of magnitude"
    - "unifying task specification in reinforcement learning"

Connected through hyperedge HE-4 (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"
    - "selective experience replay for lifelong learning"
    - "advantages and limitations of using successor features for transfer in reinforcement learning"
    - "neural episodic control"
    - "distral robust multitask reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "federated transfer reinforcement learning for autonomous driving"):
  with members:
    - "lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems"
    - "federated machine learning concept and applications"
    - "virtual to real reinforcement learning for autonomous driving"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "successor features for transfer in reinforcement learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "actor mimic deep multitask and transfer reinforcement learning"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "m 3 rl mind aware multi agent management reinforcement learning"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "simplifying reward design through divide and conquer"
    - "machine theory of mind"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "learned optimizers that scale and generalize"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"

Connected through hyperedge HE-7 (induced by source paper "universal successor features approximators"):
  with members:
    - "transfer in deep reinforcement learning using successor features and generalised policy improvement"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "unicorn continual learning with a universal off policy agent"
    - "hindsight experience replay"
    - "progressive neural networks"
    - "feudal networks for hierarchical reinforcement learning"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "grounded language learning in a simulated 3d world"

Connected through hyperedge HE-8 (induced by source paper "dynamic weights in multi objective deep reinforcement learning"):
  with members:
    - "selective experience replay for lifelong learning"
    - "combining deep reinforcement learning and safety based control for autonomous driving"
    - "successor features for transfer in reinforcement learning"
    - "multi objective deep reinforcement learning"
    - "prioritized experience replay"
    - "actor mimic deep multitask and transfer reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q 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)