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 "linear stochastic bandits under safety constraints"):
  Abstract: Bandit algorithms have various application in safety-critical systems, where it is important to respect the system constraints that rely on the bandit's unknown parameters at every round. In this paper, we formulate a linear stochastic multi-armed bandit problem with safety constraints that depend (linearly) on an unknown parameter vector. As such, the learner is unable to identify all safe actions and must act conservatively in ensuring that her actions satisfy the safety constraint at all rounds (at least with high probability). For these bandits, we propose a new UCB-based algorithm called Safe-LUCB, which includes necessary modifications to respect safety constraints. The algorithm has two phases. During the pure exploration phase the learner chooses her actions at random from a restricted set of safe actions with the goal of learning a good approximation of the entire unknown safe set. Once this goal is achieved, the algorithm begins a safe exploration-exploitation phase where the learner gradually expands their estimate of the set of safe actions while controlling the growth of regret. We provide a general regret bound for the algorithm, as well as a problem dependent bound that is connected to the location of the optimal action within the safe set. We then propose a modified heuristic that exploits our problem dependent analysis to improve the regret.

Includes members:
  Member "stagewise safe bayesian optimization with gaussian processes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "safe linear thompson sampling with side information")
  Member "provably optimal algorithms for generalized linear contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural contextual bandits with ucb based exploration")
  Member "constrained policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust deep reinforcement learning against adversarial perturbations on observations")
  Member "bayesian optimization with safety constraints safe and automatic parameter tuning in robotics":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probabilistic safety constraints for learned high relative degree system dynamics")
  Member "linear contextual bandits with knapsacks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "budget constrained bandits over general cost and reward distributions")
  Member "algorithms with logarithmic or sublinear regret for constrained contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "achieving fairness in the stochastic multi armed bandit problem")
  Member "resourceful contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "advances in bandits with knapsacks")
  Member "bandits with knapsacks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "exploration exploitation in constrained mdps")

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)