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: "variational regret bounds for reinforcement learning"
  Abstract: We consider undiscounted reinforcement learning in Markov decision processes (MDPs) where both the reward functions and the state-transition probabilities may vary (gradually or abruptly) over time. For this problem setting, we propose an algorithm and provide performance guarantees for the regret evaluated against the optimal non-stationary policy. The upper bound on the regret is given in terms of the total variation in the MDP. This is the first variational regret bound for the general reinforcement learning setting.

Connected through hyperedge HE-1 (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"
    - "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"
    - "real time bidding by reinforcement learning in display advertising"

Connected through hyperedge HE-2 (induced by source paper "contextual blocking bandits"):
  with members:
    - "blocking bandits"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "discrepancy based algorithms for non stationary rested bandits"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "contextual bandit algorithms with supervised learning guarantees"

Connected through hyperedge HE-3 (induced by source paper "efficient policy learning for non stationary mdps under adversarial manipulation"):
  with members:
    - "reinforcement learning in non stationary environments"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "data poisoning attacks in contextual bandits"
    - "is q learning provably efficient"
    - "policy certificates towards accountable reinforcement learning"
    - "regret bounds for reinforcement learning via markov chain concentration"
    - "robust adversarial reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "autonomous exploration for navigating in non stationary cmps"):
  with members:
    - "provably efficient maximum entropy exploration"
    - "count based exploration with neural density models"
    - "surprise based intrinsic motivation for deep reinforcement learning"
    - "curiosity driven exploration by self supervised prediction"
    - "vime variational information maximizing exploration"
    - "incentivizing exploration in reinforcement learning with deep predictive models"
    - "online learning in markov decision processes with adversarially chosen transition probability distributions"

Connected through hyperedge HE-5 (induced by source paper "reinforcement learning in non stationary environments"):
  with members:
    - "learning dexterous in hand manipulation"

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)