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 "mixed strategies for robust optimization of unknown objectives"):
  Abstract: We consider robust optimization problems, where the goal is to optimize an unknown objective function against the worst-case realization of an uncertain parameter. For this setting, we design a novel sample-efficient algorithm GP-MRO, which sequentially learns about the unknown objective from noisy point evaluations. GP-MRO seeks to discover a robust and randomized mixed strategy, that maximizes the worst-case expected objective value. To achieve this, it combines techniques from online learning with nonparametric confidence bounds from Gaussian processes. Our theoretical results characterize the number of samples required by GP-MRO to discover a robust near-optimal mixed strategy for different GP kernels of interest. We experimentally demonstrate the performance of our algorithm on synthetic datasets and on human-assisted trajectory planning tasks for autonomous vehicles. In our simulations, we show that robust deterministic strategies can be overly conservative, while the mixed strategies found by GP-MRO significantly improve the overall performance.

Includes members:
  Member "cooperation aware reinforcement learning for merging in dense traffic":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "survey of deep reinforcement learning for motion planning of autonomous vehicles")
  Member "bayesian optimisation under uncertain inputs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust policy search for robot navigation with stochastic meta policies")
  Member "no regret learning in unknown games with correlated payoffs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "no regret learning from partially observed data in repeated auctions")
  Member "safe intersection management for mixed transportation systems with human driven and autonomous vehicles":
    also connected through other hyperedges:
      (none)
  Member "distributionally robust submodular maximization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a unified framework of robust submodular optimization")
  Member "hierarchical game theoretic planning for autonomous vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sequential forecasting of 100 000 points")
  Member "practical bayesian optimization in the presence of outliers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust policy search for robot navigation with stochastic meta policies")
  Member "robust optimization for non convex objectives":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distribution agnostic model agnostic meta learning")

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)