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: "an optimal online method of selecting source policies for reinforcement learning"
  Abstract: Transfer learning significantly accelerates the reinforcement learning process by exploiting relevant knowledge from previous experiences. The problem of optimally selecting source policies during the learning process is of great importance yet challenging. There has been little theoretical analysis of this problem. In this paper, we develop an optimal online method to select source policies for reinforcement learning. This method formulates online source policy selection as a multi-armed bandit problem and augments Q-learning with policy reuse. We provide theoretical guarantees of the optimal selection process and convergence to the optimal policy. In addition, we conduct experiments on a grid-based robot navigation domain to demonstrate its efficiency and robustness by comparing to the state-of-the-art transfer learning method.

Connected through hyperedge HE-1 (induced by source paper "efficient deep reinforcement learning through policy transfer"):
  with members:
    - "the termination critic"
    - "deepmind control suite"
    - "kickstarting deep reinforcement learning"
    - "context aware policy reuse"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "learning to compose skills"
    - "learnings options end to end for continuous action tasks"
    - "when waiting is not an option learning options with a deliberation cost"

Connected through hyperedge HE-2 (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"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "successor features for transfer in 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"

Connected through hyperedge HE-3 (induced by source paper "temple learning template of transitions for sample efficient multi task rl"):
  with members:
    - "sample complexity of multi task reinforcement learning"
    - "clustering markov decision processes for continual transfer"
    - "incremental model based learners with formal learning time guarantees"

Connected through hyperedge HE-4 (induced by source paper "ac teach a bayesian actor critic method for policy learning with an ensemble of suboptimal teachers"):
  with members:
    - "crossnorm normalization for off policy td reinforcement learning"
    - "towards characterizing divergence in deep q learning"
    - "reinforcement learning from imperfect demonstrations"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "residual policy learning"
    - "learning dexterous in hand manipulation"
    - "off policy deep reinforcement learning without exploration"
    - "reinforcement and imitation learning for diverse visuomotor skills"

Connected through hyperedge HE-5 (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"
    - "universal successor representations for transfer reinforcement learning"
    - "constrained policy optimization"

Connected through hyperedge HE-6 (induced by source paper "multipolar multi source policy aggregation for transfer reinforcement learning between diverse environmental dynamics"):
  with members:
    - "hierarchical reinforcement learning via advantage weighted information maximization"
    - "residual reactive navigation combining classical and learned navigation strategies for deployment in unknown environments"
    - "meta learning a survey"
    - "residual policy learning"
    - "policy transfer with strategy optimization"
    - "an introduction to deep reinforcement learning"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "residual reinforcement learning for robot control"

Connected through hyperedge HE-7 (induced by source paper "context aware policy reuse"):
  with members:
    - "target transfer q learning and its convergence analysis"
    - "meta learning shared hierarchies"
    - "learning with options that terminate off policy"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "a multi armed bandit approach for online expert selection in markov decision processes"
    - "when waiting is not an option learning options with a deliberation cost"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "the option critic architecture"

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)