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: "policy gradient for coherent risk measures"
  Abstract: Several authors have recently developed risk-sensitive policy gradient methods that augment the standard expected cost minimization problem with a measure of variability in cost. These studies have focused on specific risk-measures, such as the variance or conditional value at risk (CVaR). In this work, we extend the policy gradient method to the whole class of coherent risk measures, which is widely accepted in finance and operations research, among other fields. We consider both static and time-consistent dynamic risk measures. For static risk measures, our approach is in the spirit of policy gradient algorithms and combines a standard sampling approach with convex programming. For dynamic risk measures, our approach is actor-critic style and involves explicit approximation of value function. Most importantly, our contribution presents a unified approach to risk-sensitive reinforcement learning that generalizes and extends previous results.

Connected through hyperedge HE-1 (induced by source paper "challenges of real world reinforcement learning"):
  with members:
    - "value constrained model free continuous control"
    - "deepmind control suite"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "optimizing agent behavior over long time scales by transporting value"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "reward constrained policy optimization"

Connected through hyperedge HE-2 (induced by source paper "being optimistic to be conservative quickly learning a cvar policy"):
  with members:
    - "dopamine a research framework for deep reinforcement learning"
    - "learning based model predictive control for safe exploration"
    - "policy certificates towards accountable reinforcement learning"
    - "implicit quantile networks for distributional reinforcement learning"
    - "the potential of the return distribution for exploration in rl"
    - "automated curriculum learning for neural networks"
    - "deep reinforcement learning that matters"
    - "a distributional perspective on reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "stochastically dominant distributional reinforcement learning"):
  with members:
    - "being optimistic to be conservative quickly learning a cvar policy"
    - "virel a variational inference framework for reinforcement learning"
    - "policy optimization as wasserstein gradient flows"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "implicit quantile networks for distributional reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "a distributional perspective on reinforcement learning"
    - "distributional reinforcement learning with quantile regression"

Connected through hyperedge HE-4 (induced by source paper "risk averse trust region optimization for reward volatility reduction"):
  with members:
    - "smoothing policies and safe policy gradients"
    - "emergence of locomotion behaviours in rich environments"
    - "a unified view of entropy regularized markov decision processes"
    - "proximal policy optimization algorithms"
    - "trust region policy optimization"
    - "risk constrained reinforcement learning with percentile risk criteria"
    - "variance constrained actor critic algorithms for discounted and average reward mdps"
    - "policy gradients with variance related risk criteria"

Connected through hyperedge HE-5 (induced by source paper "cutting your losses learning fault tolerant control and optimal stopping under adverse risk"):
  with members:
    - "safe multi agent reinforcement learning for autonomous driving"
    - "safe exploration of state and action spaces in reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "risk sensitive generative adversarial imitation learning"):
  with members:
    - "simple random search provides a competitive approach to reinforcement learning"
    - "risk sensitive inverse reinforcement learning via semi and non parametric methods"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "rail risk averse imitation learning"
    - "generative adversarial imitation learning"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "trust region policy optimization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "a block coordinate ascent algorithm for mean variance optimization"):
  with members:
    - "finite sample analysis of two timescale stochastic approximation with applications to reinforcement learning"
    - "a distributional perspective on reinforcement learning"
    - "stochastic variance reduction methods for policy evaluation"
    - "trust region policy optimization"
    - "risk constrained reinforcement learning with percentile risk criteria"
    - "algorithms for cvar optimization in mdps"
    - "policy gradients with variance related risk criteria"
    - "mean variance optimization in markov decision processes"

Connected through hyperedge HE-8 (induced by source paper "risk sensitive reinforcement learning a constrained optimization viewpoint"):
  with members:
    - "weighted bandits or how bandits learn distorted values that are not expected"
    - "cumulative prospect theory meets reinforcement learning prediction and control"
    - "policy gradients with variance related risk criteria"
    - "infinite horizon policy gradient estimation"

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.AI(Artificial Intelligence)