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: "sample optimal parametric q learning using linearly additive features"
  Abstract: Consider a Markov decision process (MDP) that admits a set of state-action features, which can linearly express the process's probabilistic transition model. We propose a parametric Q-learning algorithm that finds an approximate-optimal policy using a sample size proportional to the feature dimension $K$ and invariant with respect to the size of the state space. To further improve its sample efficiency, we exploit the monotonicity property and intrinsic noise structure of the Bellman operator, provided the existence of anchor state-actions that imply implicit non-negativity in the feature space. We augment the algorithm using techniques of variance reduction, monotonicity preservation, and confidence bounds. It is proved to find a policy which is $\epsilon$-optimal from any initial state with high probability using $\widetilde{O}(K/\epsilon^2(1-\gamma)^3)$ sample transitions for arbitrarily large-scale MDP with a discount factor $\gamma\in(0,1)$. A matching information-theoretical lower bound is proved, confirming the sample optimality of the proposed method with respect to all parameters (up to polylog factors).

Connected through hyperedge HE-1 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium"):
  with members:
    - "provably efficient rl with rich observations via latent state decoding"
    - "provably efficient reinforcement learning with linear function approximation"
    - "exploration enhanced politex"
    - "emergent tool use from multi agent autocurricula"
    - "solving discounted stochastic two player games with near optimal time and sample complexity"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "frequentist regret bounds for randomized least squares value iteration"
    - "a theoretical analysis of deep q learning"

Connected through hyperedge HE-2 (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"
    - "a theoretical analysis of deep q learning"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "generative adversarial imitation learning with neural networks global optimality and convergence rate"):
  with members:
    - "on computation and generalization of generative adversarial imitation learning"
    - "provably efficient reinforcement learning with linear function approximation"
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "an improved convergence analysis of stochastic variance reduced policy gradient"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "on the global convergence of imitation learning a case for linear quadratic regulator"
    - "policy optimization provably converges to nash equilibria in zero sum linear quadratic games"
    - "provably efficient exploration in policy optimization"

Connected through hyperedge HE-4 (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"
    - "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"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"

Connected through hyperedge HE-5 (induced by source paper "q star approximation schemes for batch reinforcement learning a theoretical comparison"):
  with members:
    - "doubly robust off policy evaluation with shrinkage"
    - "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"
    - "contextual decision processes with low bellman rank are pac learnable"

Connected through hyperedge HE-6 (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"
    - "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"
    - "variance reduced q learning is minimax optimal"

Connected through hyperedge HE-7 (induced by source paper "neural proximal trust region policy optimization attains globally optimal policy"):
  with members:
    - "a theoretical analysis of deep q learning"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "neural temporal difference learning converges to global optima"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "neural policy gradient methods global optimality and rates of convergence"
    - "sample optimal parametric q learning with linear transition models"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-8 (induced by source paper "provably efficient reinforcement learning with linear function approximation"):
  with members:
    - "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"
    - "efficient exploration through bayesian deep q networks"

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)