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 reduction methods for sublinear reinforcement learning"
  Abstract: This work considers the problem of provably optimal reinforcement learning for (episodic) finite horizon MDPs, i.e. how an agent learns to maximize his/her (long term) reward in an uncertain environment. The main contribution is in providing a novel algorithm --- Variance-reduced Upper Confidence Q-learning (vUCQ) --- which enjoys a regret bound of $\widetilde{O}(\sqrt{HSAT} + H^5SA)$, where the $T$ is the number of time steps the agent acts in the MDP, $S$ is the number of states, $A$ is the number of actions, and $H$ is the (episodic) horizon time. #R##N#This is the first regret bound that is both sub-linear in the model size and asymptotically optimal. The algorithm is sub-linear in that the time to achieve $\epsilon$-average regret (for any constant $\epsilon$) is $O(SA)$, which is a number of samples that is far less than that required to learn any (non-trivial) estimate of the transition model (the transition model is specified by $O(S^2A)$ parameters). The importance of sub-linear algorithms is largely the motivation for algorithms such as $Q$-learning and other "model free" approaches. vUCQ algorithm also enjoys minimax optimal regret in the long run, matching the $\Omega(\sqrt{HSAT})$ lower bound. #R##N#Variance-reduced Upper Confidence Q-learning (vUCQ) is a successive refinement method in which the algorithm reduces the variance in $Q$-value estimates and couples this estimation scheme with an upper confidence based algorithm. Technically, the coupling of both of these techniques is what leads to the algorithm enjoying both the sub-linear regret property and the (asymptotically) optimal regret.

Connected through hyperedge HE-1 (induced by source paper "provably efficient exploration for rl with unsupervised learning"):
  with members:
    - "agnostic q learning with function approximation in deterministic systems tight bounds on approximation error and sample complexity"
    - "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"
    - "sample optimal parametric q learning using linearly additive features"
    - "a theoretical analysis of deep q learning"
    - "efficient model free reinforcement learning in metric spaces"
    - "non asymptotic gap dependent regret bounds for tabular mdps"

Connected through hyperedge HE-2 (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-3 (induced by source paper "regret bounds for discounted mdps"):
  with members:
    - "q learning with ucb exploration is sample efficient for infinite horizon mdp"
    - "is q learning provably efficient"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "rainbow combining improvements in deep reinforcement learning"
    - "proximal policy optimization algorithms"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "provably efficient q learning with function approximation via distribution shift error checking oracle"):
  with members:
    - "provably efficient rl with rich observations via latent state decoding"
    - "finite sample analysis for sarsa and q learning with linear function approximation"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "efficient model free reinforcement learning in metric spaces"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "learning to control in metric space with optimal regret"
    - "on exact computation with an infinitely wide neural net"
    - "neural tangent kernel convergence and generalization in neural networks"

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"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "finite time identification in unstable linear systems"
    - "learning linear dynamical systems via spectral filtering"

Connected through hyperedge HE-6 (induced by source paper "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"):
  with members:
    - "problem dependent reinforcement learning bounds which can identify bandit structure in mdps"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "policy certificates towards accountable reinforcement learning"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "contextual decision processes with low bellman rank are pac learnable"
    - "efficient exploration and value function generalization in deterministic systems"
    - "on the sample complexity of reinforcement learning with a generative model"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-7 (induced by source paper "mo states mo problems emergency stop mechanisms from observation"):
  with members:
    - "generative adversarial imitation from observation"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "deep q learning from demonstrations"
    - "safe reinforcement learning via shielding"
    - "generative adversarial imitation learning"
    - "safe exploration of state and action spaces in reinforcement learning"
    - "learning monocular reactive uav control in cluttered natural environments"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-8 (induced by source paper "regret minimization for reinforcement learning by evaluating the optimal bias function"):
  with members:
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "posterior sampling for large scale reinforcement learning"
    - "learning unknown markov decision processes a thompson sampling approach"
    - "pac bounds for discounted mdps"
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"

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.AI(Artificial Intelligence)