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: "on kernelized multi armed bandits"
  Abstract: We consider the stochastic bandit problem with a continuous set of arms, with the expected reward function over the arms assumed to be fixed but unknown. We provide two new Gaussian process-based algorithms for continuous bandit optimization-Improved GP-UCB (IGP-UCB) and GP-Thomson sampling (GP-TS), and derive corresponding regret bounds. Specifically, the bounds hold when the expected reward function belongs to the reproducing kernel Hilbert space (RKHS) that naturally corresponds to a Gaussian process kernel used as input by the algorithms. Along the way, we derive a new self-normalized concentration inequality for vector- valued martingales of arbitrary, possibly infinite, dimension. Finally, experimental evaluation and comparisons to existing algorithms on synthetic and real-world environments are carried out that highlight the favorable gains of the proposed strategies in many cases.

Connected through hyperedge HE-1 (induced by source paper "mixed strategies for robust optimization of unknown objectives"):
  with members:
    - "cooperation aware reinforcement learning for merging in dense traffic"
    - "bayesian optimisation under uncertain inputs"
    - "no regret learning in unknown games with correlated payoffs"
    - "safe intersection management for mixed transportation systems with human driven and autonomous vehicles"
    - "distributionally robust submodular maximization"
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "practical bayesian optimization in the presence of outliers"
    - "robust optimization for non convex objectives"

Connected through hyperedge HE-2 (induced by source paper "rapidly personalizing mobile health treatment policies with limited data"):
  with members:
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "encouraging physical activity in patients with diabetes intervention using a reinforcement learning system"
    - "context aware bandits"
    - "a gang of bandits"
    - "sparse gaussian processes for multi task learning"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "portfolio allocation for bayesian optimization"

Connected through hyperedge HE-3 (induced by source paper "safety guided deep reinforcement learning via online gaussian process estimation"):
  with members:
    - "a lyapunov based approach to safe reinforcement learning"
    - "ai safety gridworlds"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "leave no trace learning to reset for safe and autonomous reinforcement learning"
    - "constrained policy optimization"
    - "deep q learning from demonstrations"
    - "safe reinforcement learning via shielding"
    - "concrete problems in ai safety"

Connected through hyperedge HE-4 (induced by source paper "reinforcement leaning in feature space matrix bandit kernels and regret bound"):
  with members:
    - "sample optimal parametric q learning using linearly additive features"
    - "contextual markov decision processes using generalized linear models"
    - "sample optimal parametric q learning with linear transition models"
    - "is q learning provably efficient"
    - "policy certificates towards accountable reinforcement learning"
    - "online learning in kernelized markov decision processes"
    - "efficient exploration through bayesian deep q networks"
    - "safe multi agent reinforcement learning for autonomous driving"

Connected through hyperedge HE-5 (induced by source paper "learning probably approximately correct maximin strategies in simulation based games with infinite strategy spaces"):
  with members:
    - "competitive bridge bidding with deep neural networks"
    - "multiagent evaluation under incomplete information"
    - "large scale learning of agent rationality in two player zero sum games"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "a generalised method for empirical game theoretic analysis"
    - "bandit learning in concave n person games"
    - "what game are we playing end to end learning in normal and extensive form games"
    - "safe and nested subgame solving for imperfect information games"

Connected through hyperedge HE-6 (induced by source paper "safe exploration for interactive machine learning"):
  with members:
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "efficient and safe exploration in deterministic markov decision processes with unknown transition models"
    - "stagewise safe bayesian optimization with gaussian processes"
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
    - "safe exploration in finite markov decision processes with gaussian processes"
    - "bayesian optimization with safety constraints safe and automatic parameter tuning in robotics"
    - "safe exploration in markov decision processes"

Connected through hyperedge HE-7 (induced by source paper "posterior variance analysis of gaussian processes with application to average learning curves"):
  with members:
    - "gaussian process based passivation of a class of nonlinear systems with unknown dynamics"
    - "provably robust learning based approach for high accuracy tracking control of lagrangian systems"
    - "learning based model predictive control for safe exploration"
    - "safe controller optimization for quadrotors with gaussian processes"
    - "bayesian optimization with safety constraints safe and automatic parameter tuning in robotics"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

Connected through hyperedge HE-8 (induced by source paper "sampling acquisition functions for batch bayesian optimization"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "batched gaussian process bandit optimization via determinantal point processes"
    - "parallel predictive entropy search for batch global optimization of expensive objective functions"
    - "parallelizing exploration exploitation tradeoffs with gaussian process bandit optimization"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "portfolio allocation for bayesian optimization"

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)