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 with linear transition models"
  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"
    - "sample optimal parametric q learning using linearly additive features"
    - "frequentist regret bounds for randomized least squares value iteration"

Connected through hyperedge HE-2 (induced by source paper "learning near optimal policies with low inherent bellman error"):
  with members:
    - "provably efficient reinforcement learning with linear function approximation"
    - "on the optimality of sparse model based planning for markov decision processes"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "frequentist regret bounds for randomized least squares value iteration"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "worst case regret bounds for exploration via randomized value functions"
    - "comments on the du kakade wang yang lower bounds"
    - "tight regret bounds for model based reinforcement learning with greedy policies"

Connected through hyperedge HE-3 (induced by source paper "neural proximal trust region policy optimization attains globally optimal policy"):
  with members:
    - "sample optimal parametric q learning using linearly additive features"
    - "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"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-4 (induced by source paper "can agents learn by analogy an inferable model for pac reinforcement learning"):
  with members:
    - "provably efficient reinforcement learning with linear function approximation"
    - "sample optimal parametric q learning using linearly additive features"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "policy certificates towards accountable reinforcement learning"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "reinforcement learning of pomdp s using spectral methods"
    - "reinforcement learning in rich observation mdps using spectral methods"
    - "reinforcement learning of pomdps using spectral methods"

Connected through hyperedge HE-5 (induced by source paper "frequentist regret bounds for randomized least squares value iteration"):
  with members:
    - "learning near optimal policies with low inherent bellman error"
    - "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"
    - "worst case regret bounds for exploration via randomized value functions"
    - "comments on the du kakade wang yang lower bounds"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "is a good representation sufficient for sample efficient reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "reinforcement leaning in feature space matrix bandit kernels and regret bound"):
  with members:
    - "sample optimal parametric q learning using linearly additive features"
    - "contextual markov decision processes using generalized linear models"
    - "is q learning provably efficient"
    - "policy certificates towards accountable reinforcement learning"
    - "online learning in kernelized markov decision processes"
    - "efficient exploration through bayesian deep q networks"
    - "on kernelized multi armed bandits"
    - "safe multi agent reinforcement learning for autonomous driving"

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"
    - "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"
    - "variance reduced value iteration and faster algorithms for solving markov decision processes"
    - "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 "state aggregation learning from markov transition data"):
  with members:
    - "on learning markov chains"
    - "scalable bilinear pi learning using state and action features"
    - "learning arbitrary statistical mixtures of discrete distributions"
    - "on learning parametric output hmms"
    - "learning mixtures of arbitrary distributions over large discrete domains"
    - "learning topic models going beyond svd"

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)