human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "risk averse multi armed bandit problems under mean variance measure"):
  Abstract: The multi-armed bandit (MAB) problems have been studied mainly under the measure of  expected  total reward accrued over a horizon of length   $T$  . In this paper, we address the issue of  risk  in MAB problems and develop parallel results under the measure of mean-variance, a commonly adopted risk measure in economics and mathematical finance. We show that the model-specific regret and the model-independent regret in terms of the mean-variance of the reward process are lower bounded by   $\Omega (\text{log}\, T)$   and   $\Omega (T^{2/3})$  , respectively. We then show that variations of the UCB policy and the DSEE policy developed for the classic risk-neutral MAB achieve these lower bounds.

Includes members:
  Member "exploration vs exploitation vs safety risk averse multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "risk averse explore then commit algorithms for finite time bandits")
  Member "generalized risk aversion in stochastic multi armed bandits":
    also connected through other hyperedges:
      (none)
  Member "risk aversion in multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive sampling for stochastic risk averse learning")
  Member "linear bandits in high dimension and recommendation systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "contextual bandits with random projection")
  Member "regret analysis of stochastic and nonstochastic multi armed bandit problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")
  Member "extended ucb policy for multi armed bandit with light tailed reward distributions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on distributed cooperative decision making in multiarmed bandits")

Question: Please tell me which class the source paper 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)