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: "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"
  Abstract: We provide an algorithm that achieves the optimal regret rate in an unknown weakly communicating Markov Decision Process (MDP). The algorithm proceeds in episodes where, in each episode, it picks a policy using regularization based on the span of the optimal bias vector. For an MDP with S states and A actions whose optimal bias vector has span bounded by H, we show a regret bound of ~O(HSpAT). We also relate the span to various diameter-like quantities associated with the MDP, demonstrating how our results improve on previous regret bounds.

Connected through hyperedge HE-1 (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-2 (induced by source paper "parameter free learning for evolving markov decision processes the blessing of more optimism"):
  with members:
    - "a new algorithm for non stationary contextual bandits efficient optimal and parameter free"
    - "variational regret bounds for reinforcement learning"
    - "learning in structured mdps with convex cost functions improved regret bounds for inventory management"
    - "large scale markov decision processes with changing rewards"
    - "a sliding window algorithm for markov decision processes with arbitrarily changing rewards and transitions"
    - "learning to optimize under non stationarity"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "hedging the drift learning to optimize under non stationarity"

Connected through hyperedge HE-3 (induced by source paper "conservative exploration in reinforcement learning"):
  with members:
    - "provably efficient reinforcement learning with linear function approximation"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "a lyapunov based approach to safe reinforcement learning"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "is q learning provably efficient"
    - "constrained policy optimization"
    - "learning unknown markov decision processes a thompson sampling approach"
    - "bayesian incentive compatible bandit exploration"

Connected through hyperedge HE-4 (induced by source paper "near optimal regret bounds for stochastic shortest path"):
  with members:
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "learning in markov decision processes under constraints"):
  with members:
    - "bandits with knapsacks"

Connected through hyperedge HE-6 (induced by source paper "model free reinforcement learning in infinite horizon average reward markov decision processes"):
  with members:
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "q learning with ucb exploration is sample efficient for infinite horizon mdp"
    - "regret minimization for reinforcement learning by evaluating the optimal bias function"
    - "improved path length regret bounds for bandits"
    - "near optimal exploration exploitation in non communicating markov decision processes"
    - "more adaptive algorithms for adversarial bandits"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "tight regret bounds for model based reinforcement learning with greedy policies"):
  with members:
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "learning latent dynamics for planning from pixels"
    - "integrating planning for task completion dialogue policy learning"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "is q learning provably efficient"
    - "policy certificates towards accountable reinforcement learning"
    - "how to combine tree search methods in reinforcement learning"
    - "feedback based tree search for reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "exploration exploitation trade off in reinforcement learning on online markov decision processes with global concave rewards"):
  with members:
    - "provably efficient maximum entropy exploration"
    - "regret bounds for reinforcement learning via markov chain concentration"
    - "adversarial bandits with knapsacks"
    - "fast rates for bandit optimization with upper confidence frank wolfe"
    - "multi objective bandits optimizing the generalized gini index"
    - "linear contextual bandits with knapsacks"
    - "an efficient algorithm for contextual bandits with knapsacks and an extension to concave objectives"
    - "bandits with concave rewards and convex knapsacks"

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)