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: "will it blend composing value functions in reinforcement learning"
  Abstract: An important property for lifelong-learning agents is the ability to combine existing skills to solve unseen tasks. In general, however, it is unclear how to compose skills in a principled way. We provide a "recipe" for optimal value function composition in entropy-regularised reinforcement learning (RL) and then extend this to the standard RL setting. Composition is demonstrated in a video game environment, where an agent with an existing library of policies is able to solve new tasks without the need for further learning.

Connected through hyperedge HE-1 (induced by source paper "entropic policy composition with generalized policy improvement and divergence correction"):
  with members:
    - "transfer in deep reinforcement learning using successor features and generalised policy improvement"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deepmind control suite"
    - "composable deep reinforcement learning for robotic manipulation"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "hindsight experience replay"
    - "reinforcement learning with deep energy based policies"

Connected through hyperedge HE-2 (induced by source paper "composing entropic policies using divergence correction"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "composable deep reinforcement learning for robotic manipulation"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "hindsight experience replay"
    - "reinforcement learning with deep energy based policies"
    - "successor features for transfer in reinforcement learning"
    - "sample efficient path integral control under uncertainty"
    - "taming the noise in reinforcement learning via soft updates"

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.LG(Machine Learning)