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: "on the use of non stationary policies for stationary infinite horizon markov decision processes"
  Abstract: We consider infinite-horizon stationary $\gamma$-discounted Markov Decision Processes, for which it is known that there exists a stationary optimal policy. Using Value and Policy Iteration with some error $\epsilon$ at each iteration, it is well-known that one can compute stationary policies that are $\frac{2\gamma}{(1-\gamma)^2}\epsilon$-optimal. After arguing that this guarantee is tight, we develop variations of Value and Policy Iteration for computing non-stationary policies that can be up to $\frac{2\gamma}{1-\gamma}\epsilon$-optimal, which constitutes a significant improvement in the usual situation when $\gamma$ is close to 1. Surprisingly, this shows that the problem of "computing near-optimal non-stationary policies" is much simpler than that of "computing near-optimal stationary policies".

Connected through hyperedge HE-1 (induced by source paper "q star approximation schemes for batch reinforcement learning a theoretical comparison"):
  with members:
    - "doubly robust off policy evaluation with shrinkage"
    - "sample optimal parametric q learning using linearly additive features"
    - "minimax weight and q function learning for off policy evaluation"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "information theoretic considerations in batch reinforcement learning"
    - "a kernel loss for solving the bellman equation"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "is the bellman residual a bad proxy"

Connected through hyperedge HE-2 (induced by source paper "approximate policy iteration schemes a comparison"):
  with members:
    - "approximate modified policy iteration"

Connected through hyperedge HE-3 (induced by source paper "on the performance bounds of some policy search dynamic programming algorithms"):
  with members:
    - "approximate modified policy iteration"
    - "search based structured prediction"
    - "approximate policy iteration with a policy language bias solving relational markov decision processes"

Connected through hyperedge HE-4 (induced by source paper "policy search any local optimum enjoys a global performance guarantee"):
  with members:
    - "approximate modified policy iteration"
    - "approximate policy iteration with a policy language bias solving relational markov decision processes"

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)