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: "modeling human decision making in generalized gaussian multi armed bandits"
  Abstract: We present a formal model of human decision-making in explore-exploit tasks using the context of multi-armed bandit problems, where the decision-maker must choose among multiple options with uncertain rewards. We address the standard multi-armed bandit problem, the multi-armed bandit problem with transition costs, and the multi-armed bandit problem on graphs. We focus on the case of Gaussian rewards in a setting where the decision-maker uses Bayesian inference to estimate the reward values. We model the decision-maker's prior knowledge with the Bayesian prior on the mean reward. We develop the upper credible limit (UCL) algorithm for the standard multi-armed bandit problem and show that this deterministic algorithm achieves logarithmic cumulative expected regret, which is optimal performance for uninformative priors. We show how good priors and good assumptions on the correlation structure among arms can greatly enhance decision-making performance, even over short time horizons. We extend to the stochastic UCL algorithm and draw several connections to human decision-making behavior. We present empirical data from human experiments and show that human performance is efficiently captured by the stochastic UCL algorithm with appropriate parameters. For the multi-armed bandit problem with transition costs and the multi-armed bandit problem on graphs, we generalize the UCL algorithm to the block UCL algorithm and the graphical block UCL algorithm, respectively. We show that these algorithms also achieve logarithmic cumulative expected regret and require a sub-logarithmic expected number of transitions among arms. We further illustrate the performance of these algorithms with numerical examples.

Connected through hyperedge HE-1 (induced by source paper "the assistive multi armed bandit"):
  with members:
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "proximal policy optimization algorithms"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "high dimensional continuous control using generalized advantage estimation"
    - "learning economic parameters from revealed preferences"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "efficient bayes adaptive reinforcement learning using sample based search"

Connected through hyperedge HE-2 (induced by source paper "environmental hotspot identification in limited time with a uav equipped with a downward facing camera"):
  with members:
    - "near optimal irrevocable sample selection for periodic data streams with applications to marine robotics"

Connected through hyperedge HE-3 (induced by source paper "reward based deception with cognitive bias"):
  with members:
    - "a game theoretic taxonomy and survey of defensive deception for cybersecurity and privacy"

Connected through hyperedge HE-4 (induced by source paper "human ai learning performance in multi armed bandits"):
  with members:
    - "expressing robot incapability"
    - "game theoretic modeling of human adaptation in human robot collaboration"
    - "cooperative inverse reinforcement learning"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-5 (induced by source paper "controlling human utilization of failure prone systems via taxes"):
  with members:
    - "interdependent security games on networks under behavioral probability weighting"
    - "strategic communication between prospect theoretic agents over a gaussian test channel"
    - "prospect theory for enhanced smart grid resilience using distributed energy storage"
    - "fragility of the commons under prospect theoretic risk attitudes"

Connected through hyperedge HE-6 (induced by source paper "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms"):
  with members:
    - "on distributed cooperative decision making in multiarmed bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"

Connected through hyperedge HE-7 (induced by source paper "on distributed cooperative decision making in multiarmed bandits"):
  with members:
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"
    - "extended ucb policy for multi armed bandit with light tailed reward distributions"

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)