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: "deep conservative policy iteration"
  Abstract: Conservative Policy Iteration (CPI) is a founding algorithm of Approximate Dynamic Programming (ADP). Its core principle is to stabilize greediness through stochastic mixtures of consecutive policies. It comes with strong theoretical guarantees, and inspired approaches in deep Reinforcement Learning (RL). However, CPI itself has rarely been implemented, never with neural networks, and only experimented on toy problems. In this paper, we show how CPI can be practically combined with deep RL with discrete actions. We also introduce adaptive mixture rates inspired by the theory. We experiment thoroughly the resulting algorithm on the simple Cartpole problem, and validate the proposed method on a representative subset of Atari games. Overall, this work suggests that revisiting classic ADP may lead to improved and more stable deep RL algorithms.

Connected through hyperedge HE-1 (induced by source paper "stable policy optimization via off policy divergence regularization"):
  with members:
    - "imitation learning via off policy distribution matching"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "truly proximal policy optimization"
    - "a theory of regularized markov decision processes"
    - "algaedice policy gradient from arbitrary experience"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "on convergence and stability of gans"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"

Connected through hyperedge HE-2 (induced by source paper "modified actor critics"):
  with members:
    - "a theory of regularized markov decision processes"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "relative entropy regularized policy iteration"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "reinforcement learning with deep energy based policies"
    - "proximal policy optimization algorithms"
    - "convergent tree backup and retrace with function approximation"

Connected through hyperedge HE-3 (induced by source paper "on connections between constrained optimization and reinforcement learning"):
  with members:
    - "adaptive trust region policy optimization global convergence and faster rates for regularized mdps"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "a theory of regularized markov decision processes"
    - "a unified view of entropy regularized markov decision processes"
    - "is the bellman residual a bad proxy"
    - "trust region policy optimization"

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)