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 explore then commit algorithms for finite time bandits"):
  Abstract: In this paper, we study multi-armed bandit problems in explore-then-commit setting. In our proposed explore-then-commit setting, the goal is to identify the best arm after a pure experimentation (exploration) phase and exploit it once or for a given finite number of times. We identify that although the arm with the highest expected reward is the most desirable objective for infinite exploitations, it is not necessarily the one that is most probable to have the highest reward in a single or finite-time exploitations. Alternatively, we advocate the idea of risk-aversion where the objective is to compete against the arm with the best risk-return trade-off. Then, we propose two algorithms whose objectives are to select the arm that is most probable to reward the most. Using a new notion of finite-time exploitation regret, we find an upper bound for the minimum number of experiments before commitment, to guarantee an upper bound for the regret. As compared to existing risk-averse bandit algorithms, our algorithms do not rely on hyper-parameters, resulting in a more robust behavior in practice, which is verified by the numerical evaluation.

Includes members:
  Member "risk aware multi armed bandits using conditional value at risk":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "thompson sampling algorithms for mean variance bandits")
  Member "blind gb pandas a blind throughput optimal load balancing algorithm for affinity scheduling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the power of d choices in scheduling for data centers with heterogeneous servers")
  Member "gb pandas throughput and heavy traffic optimality analysis for affinity scheduling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the power of d choices in scheduling for data centers with heterogeneous servers")
  Member "near data scheduling for data centers with multiple levels of data locality":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the power of d choices in scheduling for data centers with heterogeneous servers")
  Member "distributed user association in energy harvesting dense small cell networks a mean field multi armed bandit approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning and fairness in energy harvesting a maximin multi armed bandits approach")
  Member "stochastic multi armed bandits in constant space":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "memory constrained no regret learning in adversarial bandits")
  Member "risk averse multi armed bandit problems under mean variance measure":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "thompson sampling algorithms for mean variance bandits")
  Member "a survey on contextual multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adaptive metamorphic testing with contextual 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)