human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on connections between constrained optimization and reinforcement learning"):
  Abstract: Dynamic Programming (DP) provides standard algorithms to solve Markov Decision Processes. However, these algorithms generally do not optimize a scalar objective function. In this paper, we draw connections between DP and (constrained) convex optimization. Specifically, we show clear links in the algorithmic structure between three DP schemes and optimization algorithms. We link Conservative Policy Iteration to Frank-Wolfe, Mirror-Descent Modified Policy Iteration to Mirror Descent, and Politex (Policy Iteration Using Expert Prediction) to Dual Averaging. These abstract DP schemes are representative of a number of (deep) Reinforcement Learning (RL) algorithms. By highlighting these connections (most of which have been noticed earlier, but in a scattered way), we would like to encourage further studies linking RL and convex optimization, that could lead to the design of new, more efficient, and better understood RL algorithms.

Includes members:
  Member "adaptive trust region policy optimization global convergence and faster rates for regularized mdps":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "policy aware model learning for policy gradient methods")
  Member "optimality and approximation with policy gradient methods in markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "upper confidence primal dual optimization stochastically constrained markov decision processes with adversarial losses and unknown transitions")
  Member "deep conservative policy iteration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stable policy optimization via off policy divergence regularization")
  Member "a theory of regularized markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "a unified view of entropy regularized markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reinforcement learning via fenchel rockafellar duality")
  Member "is the bellman residual a bad proxy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "q star approximation schemes for batch reinforcement learning a theoretical comparison")
  Member "trust region policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

Question: Please tell me which class the source paper 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)