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: "online learning in markov decision processes with adversarially chosen transition probability distributions"
  Abstract: We study the problem of learning Markov decision processes with finite state and action spaces when the transition probability distributions and loss functions are chosen adversarially and are allowed to change with time. We introduce an algorithm whose regret with respect to any policy in a comparison class grows as the square root of the number of rounds of the game, provided the transition probabilities satisfy a uniform mixing condition. Our approach is efficient as long as the comparison class is polynomial and we can compute expectations over sample paths for each policy. Designing an efficient algorithm with small regret for the general case remains an open problem.

Connected through hyperedge HE-1 (induced by source paper "combining no regret and q learning"):
  with members:
    - "vortices instead of equilibria in minmax optimization chaos and butterfly effects of online learning in zero sum games"
    - "finite regret and cycles with fixed step size via alternating gradient descent ascent"
    - "stable predictive optimistic counterfactual regret minimization"
    - "learning to collaborate in markov decision processes"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "regret circuits composability of regret minimizers"
    - "solving imperfect information games via discounted regret minimization"
    - "deep counterfactual regret minimization"

Connected through hyperedge HE-2 (induced by source paper "learning to collaborate in markov decision processes"):
  with members:
    - "modeling others using oneself in multi agent reinforcement learning"
    - "an overview of machine teaching"
    - "online reinforcement learning in stochastic games"
    - "game theoretic modeling of human adaptation in human robot collaboration"
    - "corralling a band of bandit algorithms"
    - "prediction with a short memory"
    - "fast convergence of regularized learning in games"
    - "noise tolerant learning the parity problem and the statistical query model"

Connected through hyperedge HE-3 (induced by source paper "autonomous exploration for navigating in non stationary cmps"):
  with members:
    - "variational regret bounds for reinforcement learning"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "large scale markov decision processes with changing rewards"):
  with members:
    - "introduction to online convex optimization"
    - "a sliding window algorithm for markov decision processes with arbitrarily changing rewards and transitions"
    - "scalable bilinear pi learning using state and action features"
    - "primal dual π learning sample complexity and sublinear run time for ergodic markov decision problems"
    - "a unified view of entropy regularized markov decision processes"
    - "online markov decision processes with policy iteration"

Connected through hyperedge HE-5 (induced by source paper "drifting reinforcement learning the blessing of more optimism in face of endogenous exogenous dynamics"):
  with members:
    - "learning to optimize under non stationarity"
    - "online optimization competing with dynamic comparators"
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-6 (induced by source paper "on greedy and strategic evaders in sequential interdiction settings with incomplete information"):
  with members:
    - "a game of drones cyber physical security of time critical uav applications with cumulative prospect theory perceptions and valuations"
    - "prospect theory for enhanced cyber physical security of drone delivery systems a network interdiction game"
    - "optimal interdiction of unreactive markovian evaders"

Connected through hyperedge HE-7 (induced by source paper "regularised deep reinforcement learning with guaranteed convergence"):
  with members:
    - "decentralised learning in systems with many many strategic agents"
    - "an information theoretic on line update principle for perception action coupling"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "balancing two player stochastic games with soft q learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "equivalence between policy gradients and soft q learning"
    - "bridging the gap between value and policy based reinforcement learning"
    - "reinforcement learning with deep energy based policies"

Connected through hyperedge HE-8 (induced by source paper "analysis of hannan consistent selection for monte carlo tree search in simultaneous move games"):
  with members:
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "bandit algorithms for tree search"
    - "convergence of monte carlo tree search in simultaneous move games"
    - "computer solution to the game of pure strategy"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "computing approximate nash equilibria and robust best responses using sampling"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

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)