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: "risk sensitive reinforcement learning"
  Abstract: We derive a family of risk-sensitive reinforcement learning methods for agents, who face sequential decision-making tasks in uncertain environments. By applying a utility function to the temporal difference TD error, nonlinear transformations are effectively applied not only to the received rewards but also to the true transition probabilities of the underlying Markov decision process. When appropriate utility functions are chosen, the agents' behaviors express key features of human behavior as predicted by prospect theory Kahneman & Tversky, 1979, for example, different risk preferences for gains and losses, as well as the shape of subjective probability curves. We derive a risk-sensitive Q-learning algorithm, which is necessary for modeling human behavior when transition probabilities are unknown, and prove its convergence. As a proof of principle for the applicability of the new framework, we apply it to quantify human behavior in a sequential investment task. We find that the risk-sensitive variant provides a significantly better fit to the behavioral data and that it leads to an interpretation of the subject's responses that is indeed consistent with prospect theory. The analysis of simultaneously measured fMRI signals shows a significant correlation of the risk-sensitive TD error with BOLD signal change in the ventral striatum. In addition we find a significant correlation of the risk-sensitive Q-values with neural activity in the striatum, cingulate cortex, and insula that is not present if standard Q-values are used.

Connected through hyperedge HE-1 (induced by source paper "fully bayesian recurrent neural networks for safe reinforcement learning"):
  with members:
    - "model free deep reinforcement learning for urban autonomous driving"
    - "safe reinforcement learning with model uncertainty estimates"
    - "social attention modeling attention in human crowds"
    - "bayesian recurrent neural networks"
    - "uncertainty aware reinforcement learning for collision avoidance"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "concrete problems in ai safety"

Connected through hyperedge HE-2 (induced by source paper "a scheme for dynamic risk sensitive sequential decision making"):
  with members:
    - "quota the quantile option architecture for reinforcement learning"
    - "state augmentation transformations for risk sensitive reinforcement learning"
    - "a lyapunov based approach to safe reinforcement learning"
    - "implicit quantile networks for distributional reinforcement learning"
    - "quantile reinforcement learning"
    - "transition based versus state based reward functions for mdps with value at risk"
    - "risk constrained reinforcement learning with percentile risk criteria"
    - "mean variance optimization in markov decision processes"

Connected through hyperedge HE-3 (induced by source paper "variance based risk estimations in markov processes via transformation with state lumping"):
  with members:
    - "state augmentation transformations for risk sensitive reinforcement learning"
    - "risk constrained reinforcement learning with percentile risk criteria"
    - "mean variance optimization in markov decision processes"

Connected through hyperedge HE-4 (induced by source paper "safe reinforcement learning with model uncertainty estimates"):
  with members:
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "spatial uncertainty sampling for end to end control"
    - "fully distributed multi robot collision avoidance via deep reinforcement learning for safe and efficient navigation in complex scenarios"
    - "stein variational policy gradient"
    - "gp ilqg data driven robust optimal control for uncertain nonlinear dynamical systems"
    - "social attention modeling attention in human crowds"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "socially aware motion planning with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "rail risk averse imitation learning"):
  with members:
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "third person imitation learning"
    - "on a formal model of safe and scalable self driving cars"
    - "end to end learning for self driving cars"
    - "generative adversarial imitation learning"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "trust region policy optimization"

Connected through hyperedge HE-6 (induced by source paper "risk sensitive inverse reinforcement learning via semi and non parametric methods"):
  with members:
    - "how should a robot assess risk towards an axiomatic theory of risk in robotics"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "risk sensitive and robust decision making a cvar optimization approach"
    - "maximum entropy deep inverse reinforcement learning"
    - "cumulative prospect theory meets reinforcement learning prediction and control"
    - "computational rationalization the inverse equilibrium problem"
    - "continuous inverse optimal control with locally optimal examples"
    - "policy gradients with variance related risk criteria"

Connected through hyperedge HE-7 (induced by source paper "particle value functions"):
  with members:
    - "particle smoothing for hidden diffusion processes adaptive path integral smoother"
    - "importance weighted autoencoders"
    - "taming the noise in reinforcement learning via soft updates"
    - "risk sensitive path integral control"

Connected through hyperedge HE-8 (induced by source paper "inverse risk sensitive reinforcement learning"):
  with members:
    - "cumulative prospect theory meets reinforcement learning prediction and control"
    - "apprenticeship learning using inverse reinforcement learning and gradient methods"
    - "risk sensitive reinforcement learning applied to control under constraints"

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)