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: "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.

Connected through hyperedge HE-1 (induced by source paper "agnostic q learning with function approximation in deterministic systems tight bounds on approximation error and sample complexity"):
  with members:
    - "efficient and robust algorithms for adversarial linear contextual bandits"
    - "provably efficient reinforcement learning with linear function approximation"
    - "on the optimality of sparse model based planning for markov decision processes"
    - "sample optimal parametric q learning using linearly additive features"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "finite sample analysis for sarsa with linear function approximation"
    - "provably efficient q learning with function approximation via distribution shift error checking oracle"
    - "comments on the du kakade wang yang lower bounds"

Connected through hyperedge HE-2 (induced by source paper "provably efficient exploration in policy optimization"):
  with members:
    - "provably efficient reinforcement learning with linear function approximation"
    - "exploration enhanced politex"
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "sample optimal parametric q learning using linearly additive features"
    - "a theoretical analysis of deep q learning"
    - "online convex optimization in adversarial markov decision processes"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "sqrt n regret for learning in markov decision processes with function approximation and low bellman rank"

Connected through hyperedge HE-3 (induced by source paper "provably efficient reinforcement learning with linear function approximation"):
  with members:
    - "sample optimal parametric q learning using linearly additive features"
    - "variance reduced q learning is minimax optimal"
    - "provably efficient q learning with function approximation via distribution shift error checking oracle"
    - "learning linear quadratic regulators efficiently with only t regret"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "lipschitz bandit optimization with improved efficiency"
    - "model free linear quadratic control via reduction to expert prediction"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"

Connected through hyperedge HE-4 (induced by source paper "sqrt n regret for learning in markov decision processes with function approximation and low bellman rank"):
  with members:
    - "provably efficient rl with rich observations via latent state decoding"
    - "provably efficient reinforcement learning with linear function approximation"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "stochastic approximation with cone contractive operators sharp ell_ infty bounds for q learning"
    - "information theoretic considerations in batch reinforcement learning"
    - "on oracle efficient pac rl with rich observations"
    - "is q learning provably efficient"
    - "global convergence of policy gradient methods for linearized control problems"

Connected through hyperedge HE-5 (induced by source paper "learning linear dynamical systems with semi parametric least squares"):
  with members:
    - "non asymptotic identification of lti systems from a single trajectory"
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "spectral filtering for general linear dynamical systems"
    - "learning without mixing towards a sharp analysis of linear system identification"
    - "variance reduction methods for sublinear reinforcement learning"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "finite time identification in unstable linear systems"

Connected through hyperedge HE-6 (induced by source paper "variance reduced q learning is minimax optimal"):
  with members:
    - "stochastic approximation with cone contractive operators sharp ell_ infty bounds for q learning"
    - "learning linear quadratic regulators efficiently with only t regret"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "is q learning provably efficient"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "stochastic variance reduction methods for policy evaluation"

Connected through hyperedge HE-7 (induced by source paper "feature based q learning for two player stochastic games"):
  with members:
    - "sample optimal parametric q learning using linearly additive features"
    - "a theoretical analysis of deep q learning"
    - "sample optimal parametric q learning with linear transition models"
    - "finite sample analyses for fully decentralized multi agent reinforcement learning"
    - "finite sample analysis for decentralized batch multi agent reinforcement learning with networked agents"
    - "scalable bilinear pi learning using state and action features"
    - "contextual decision processes with low bellman rank are pac learnable"
    - "strategy iteration is strongly polynomial for 2 player turn based stochastic games with a constant discount factor"

Connected through hyperedge HE-8 (induced by source paper "efficient model free reinforcement learning in metric spaces"):
  with members:
    - "introduction to multi armed bandits"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "is q learning provably efficient"
    - "model based rl in contextual decision processes pac bounds and exponential improvements over model free approaches"
    - "exploration a study of count based exploration for deep reinforcement learning"
    - "trust region policy optimization"
    - "online regret bounds for undiscounted continuous reinforcement learning"

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.DS(Data Structures and Algorithms)