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 "variance reduced value iteration and faster algorithms for solving markov decision processes"):
  Abstract: In this paper we provide faster algorithms for approximately solving discounted Markov Decision Processes in multiple parameter regimes. Given a discounted Markov Decision Process (DMDP) with $|S|$ states, $|A|$ actions, discount factor $\gamma\in(0,1)$, and rewards in the range $[-M, M]$, we show how to compute an $\epsilon$-optimal policy, with probability $1 - \delta$ in time \[ \tilde{O} \left( \left(|S|^2 |A| + \frac{|S| |A|}{(1 - \gamma)^3} \right) #R##N#\log\left( \frac{M}{\epsilon} \right) \log\left( \frac{1}{\delta} \right) \right) \] This contribution reflects the first nearly linear time, nearly linearly convergent algorithm for solving DMDP's for intermediate values of $\gamma$. #R##N#We also show how to obtain improved sublinear time algorithms and provide an algorithm which computes an $\epsilon$-optimal policy with probability $1 - \delta$ in time #R##N#\[ \tilde{O} \left(\frac{|S| |A| M^2}{(1 - \gamma)^4 \epsilon^2} \log \left(\frac{1}{\delta}\right) \right) #R##N#\] provided we can sample from the transition function in $O(1)$ time. #R##N#Interestingly, we obtain our results by a careful modification of approximate value iteration. We show how to combine classic approximate value iteration analysis with new techniques in variance reduction. Our fastest algorithms leverage further insights to ensure that our algorithms make monotonic progress towards the optimal value. This paper is one of few instances in using sampling to obtain a linearly convergent linear programming algorithm and we hope that the analysis may be useful more broadly.

Includes members:
  Member "lower bound on the computational complexity of discounted markov decision problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "primal dual π learning sample complexity and sublinear run time for ergodic markov decision problems")
  Member "on the complexity of policy iteration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "solving discounted stochastic two player games with near optimal time and sample complexity")
  Member "on the sample complexity of reinforcement learning with a generative model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning near optimal policies with low inherent bellman error")
  Member "strategy iteration is strongly polynomial for 2 player turn based stochastic games with a constant discount factor":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium")

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.DS(Data Structures and Algorithms)